Conversational Database Analysis

ABSTRACT

Systems and methods for conversational user experiences and conversational database analysis disclosed herein improve the efficiency and accessibility of low-latency database analytics. The method may include obtaining data expressing a usage intent with respect to the low-latency database analysis system, wherein the data expressing the usage intent includes a current request string expressed in a natural language, a current context associated with the current request string, and a previously generated context associated with a previously generated resolved-request, identifying, from the current request string, a conversational phrase corresponding to a conversational phrase pattern from a defined set of conversational phrase patterns, generating a resolved-request based on the identified conversational phrase, including the resolved-request in the current context, obtaining results data responsive to the resolved-request from a distributed in-memory database, generating a response including the results data and the current context, and outputting the response.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalPatent Application No. 62/760,443, filed Nov. 13, 2018, the entiredisclosure of which is incorporated by reference herein.

BACKGROUND

Advances in computer storage and database technology have led toexponential growth of the amount of data being created. Businesses areoverwhelmed by the volume of the data stored in their computer systems.Existing database analytic tools are inefficient, costly to utilize,and/or require substantial configuration and training.

SUMMARY

Disclosed herein are implementations of methods for conversational userexperiences using low-latency database analysis and systems forconversational database analysis. The method may include obtaining dataexpressing a usage intent with respect to the low-latency databaseanalysis system, wherein the data expressing the usage intent includes acurrent request string expressed in a natural language, a currentcontext associated with the current request string, and a previouslygenerated context associated with a previously generatedresolved-request, identifying, from the current request string, aconversational phrase corresponding to a conversational phrase patternfrom a defined set of conversational phrase patterns, generating aresolved-request based on the identified conversational phrase,including the resolved-request in the current context, obtaining resultsdata responsive to the resolved-request from a distributed in-memorydatabase, generating a response including the results data and thecurrent context, and outputting the response.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detaileddescription when read in conjunction with the accompanying drawings. Itis emphasized that, according to common practice, the various featuresof the drawings are not to-scale. On the contrary, the dimensions of thevarious features are arbitrarily expanded or reduced for clarity.

FIG. 1 is a block diagram of an example of a computing device.

FIG. 2 is a block diagram of an example of a computing system.

FIG. 3 is a block diagram of an example of a low-latency databaseanalysis system.

FIG. 4 is a block diagram of an example of a low-latency databaseanalysis system in accordance with this disclosure.

FIG. 5 is a flow diagram of an example of a method forrequest-transformation phrase processing in accordance with thisdisclosure.

FIG. 6 is a flow diagram of an example of a method for serial-requestphrase processing in accordance with this disclosure.

FIG. 7 is a flow diagram of an example of a method forautonomous-analysis phrase processing in accordance with thisdisclosure.

FIG. 8 is a flow diagram of an example of a method for action phraseprocessing in accordance with this disclosure.

FIG. 9 is a diagram of an example parsing of an input sentence inaccordance with this disclosure.

FIG. 10 is a block diagram of an example of pattern matchers inaccordance with this disclosure.

FIG. 11 is a flow diagram of an example of a method for conversationaldatabase analysis in accordance with this disclosure.

DETAILED DESCRIPTION

Businesses and other organizations store large amounts of data, such asbusiness records, transaction records, and the like, in data storagesystems, such as relational database systems that store data as records,or rows, having values, or fields, corresponding to respective columnsin tables that can be interrelated using key values. Databasesstructures are often normalized or otherwise organized to maximize datadensity and to maximize transactional data operations at the expense ofincreased complexity and reduced accessibility for analysis. Individualrecords and tables may have little or no utility without substantialcorrelation, interpretation, and analysis. The complexity of these datastructures and the large volumes of data that can be stored thereinlimit the accessibility of the data and require substantial skilledhuman resources to code procedures and tools that allow business usersto access useful data. The tools that are available for accessing thesesystems are limited to outputting data expressly requested by the usersand lack the capability to identify and prioritize data other than thedata expressly requested. Useful data, such as data aggregations,patterns, and statistical anomalies that would not be available insmaller data sets (e.g., 10,000 rows of data), and may not be apparentto human users, may be derivable using the large volume of data (e.g.,millions or billions of rows) stored in complex data storage systems,such as relational database systems, and may be inaccessible due to thecomplexity and limitations of the data storage systems.

The systems and methods for conversational user experiences andconversational database analysis disclosed herein improve the efficiencyand accessibility of low-latency database analytics by providingdatabase access in response to conversational input. A conversationaldatabase analysis system receives conversational user inputs based on,such as natural language, or near-natural language, input, and maintainsdata to correlate temporally successive input instances as a dialog, orsequence of contexts. In an example, the conversational databaseanalysis system parses conversational user input to identify elementsand structure of the conversational user input, relates the identifiedelements and structure of the conversational user input to the databaseand to available data regarding a previously generated dialog, orsequence of contexts, to identify a probable intent of theconversational user input and to obtain output data responsive to theconversational user input. Multiple contexts may be accumulated tomaintain a conversational context flow for subsequent user inputs whichmodify or operate against previous contexts.

FIG. 1 is a block diagram of an example of a computing device 1000. Oneor more aspects of this disclosure may be implemented using thecomputing device 1000. The computing device 1000 includes a processor1100, static memory 1200, low-latency memory 1300, an electroniccommunication unit 1400, a user interface 1500, a bus 1600, and a powersource 1700. Although shown as a single unit, any one or more element ofthe computing device 1000 may be integrated into any number of separatephysical units. For example, the low-latency memory 1300 and theprocessor 1100 may be integrated in a first physical unit and the userinterface 1500 may be integrated in a second physical unit. Although notshown in FIG. 1, the computing device 1000 may include other aspects,such as an enclosure or one or more sensors.

The computing device 1000 may be a stationary computing device, such asa personal computer (PC), a server, a workstation, a minicomputer, or amainframe computer; or a mobile computing device, such as a mobiletelephone, a personal digital assistant (PDA), a laptop, or a tablet PC.

The processor 1100 may include any device or combination of devicescapable of manipulating or processing a signal or other information,including optical processors, quantum processors, molecular processors,or a combination thereof. The processor 1100 may be a central processingunit (CPU), such as a microprocessor, and may include one or moreprocessing units, which may respectively include one or more processingcores. The processor 1100 may include multiple interconnectedprocessors. For example, the multiple processors may be hardwired ornetworked, including wirelessly networked. In some implementations, theoperations of the processor 1100 may be distributed across multiplephysical devices or units that may be coupled directly or across anetwork. In some implementations, the processor 1100 may include acache, or cache memory, for internal storage of operating data orinstructions. The processor 1100 may include one or more special purposeprocessors, one or more digital signal processor (DSP), one or moremicroprocessors, one or more controllers, one or more microcontrollers,one or more integrated circuits, one or more an Application SpecificIntegrated Circuits, one or more Field Programmable Gate Array, one ormore programmable logic arrays, one or more programmable logiccontrollers, firmware, one or more state machines, or any combinationthereof.

The processor 1100 may be operatively coupled with the static memory1200, the low-latency memory 1300, the electronic communication unit1400, the user interface 1500, the bus 1600, the power source 1700, orany combination thereof. The processor may execute, which may includecontrolling, such as by sending electronic signals to, receivingelectronic signals from, or both, the static memory 1200, thelow-latency memory 1300, the electronic communication unit 1400, theuser interface 1500, the bus 1600, the power source 1700, or anycombination thereof to execute, instructions, programs, code,applications, or the like, which may include executing one or moreaspects of an operating system, and which may include executing one ormore instructions to perform one or more aspects described herein, aloneor in combination with one or more other processors.

The static memory 1200 is coupled to the processor 1100 via the bus 1600and may include non-volatile memory, such as a disk drive, or any formof non-volatile memory capable of persistent electronic informationstorage, such as in the absence of an active power supply. Althoughshown as a single block in FIG. 1, the static memory 1200 may beimplemented as multiple logical or physical units.

The static memory 1200 may store executable instructions or data, suchas application data, an operating system, or a combination thereof, foraccess by the processor 1100. The executable instructions may beorganized into programmable modules or algorithms, functional programs,codes, code segments, or combinations thereof to perform one or moreaspects, features, or elements described herein. The application datamay include, for example, user files, database catalogs, configurationinformation, or a combination thereof. The operating system may be, forexample, a desktop or laptop operating system; an operating system for amobile device, such as a smartphone or tablet device; or an operatingsystem for a large device, such as a mainframe computer.

The low-latency memory 1300 is coupled to the processor 1100 via the bus1600 and may include any storage medium with low-latency data accessincluding, for example, DRAM modules such as DDR SDRAM, Phase-ChangeMemory (PCM), flash memory, or a solid-state drive. Although shown as asingle block in FIG. 1, the low-latency memory 1300 may be implementedas multiple logical or physical units. Other configurations may be used.For example, low-latency memory 1300, or a portion thereof, andprocessor 1100 may be combined, such as by using a system on a chipdesign.

The low-latency memory 1300 may store executable instructions or data,such as application data for low-latency access by the processor 1100.The executable instructions may include, for example, one or moreapplication programs, that may be executed by the processor 1100. Theexecutable instructions may be organized into programmable modules oralgorithms, functional programs, codes, code segments, and/orcombinations thereof to perform various functions described herein.

The low-latency memory 1300 may be used to store data that is analyzedor processed using the systems or methods described herein. For example,storage of some or all data in low-latency memory 1300 instead of staticmemory 1200 may improve the execution speed of the systems and methodsdescribed herein by permitting access to data more quickly by an orderof magnitude or greater (e.g., nanoseconds instead of microseconds).

The electronic communication unit 1400 is coupled to the processor 1100via the bus 1600. The electronic communication unit 1400 may include oneor more transceivers. The electronic communication unit 1400 may, forexample, provide a connection or link to a network via a networkinterface. The network interface may be a wired network interface, suchas Ethernet, or a wireless network interface. For example, the computingdevice 1000 may communicate with other devices via the electroniccommunication unit 1400 and the network interface using one or morenetwork protocols, such as Ethernet, Transmission ControlProtocol/Internet Protocol (TCP/IP), power line communication (PLC),Wi-Fi, infrared, ultra violet (UV), visible light, fiber optic, wireline, general packet radio service (GPRS), Global System for Mobilecommunications (GSM), code-division multiple access (CDMA), Long-TermEvolution (LTE), or other suitable protocols.

The user interface 1500 may include any unit capable of interfacing witha human user, such as a virtual or physical keypad, a touchpad, adisplay, a touch display, a speaker, a microphone, a video camera, asensor, a printer, or any combination thereof. For example, a keypad canconvert physical input of force applied to a key to an electrical signalthat can be interpreted by computing device 1000. In another example, adisplay can convert electrical signals output by computing device 1000to light. The purpose of such devices may be to permit interaction witha human user, for example by accepting input from the human user andproviding output back to the human user. The user interface 1500 mayinclude a display; a positional input device, such as a mouse, touchpad,touchscreen, or the like; a keyboard; or any other human and machineinterface device. The user interface 1500 may be coupled to theprocessor 1100 via the bus 1600. In some implementations, the userinterface 1500 can include a display, which can be a liquid crystaldisplay (LCD), a cathode-ray tube (CRT), a light emitting diode (LED)display, an organic light emitting diode (OLED) display, an activematrix organic light emitting diode (AMOLED), or other suitable display.In some implementations, the user interface 1500, or a portion thereof,may be part of another computing device (not shown). For example, aphysical user interface, or a portion thereof, may be omitted from thecomputing device 1000 and a remote or virtual interface may be used,such as via the electronic communication unit 1400.

The bus 1600 is coupled to the static memory 1200, the low-latencymemory 1300, the electronic communication unit 1400, the user interface1500, and the power source 1700. Although a single bus is shown in FIG.1, the bus 1600 may include multiple buses, which may be connected, suchas via bridges, controllers, or adapters.

The power source 1700 provides energy to operate the computing device1000. The power source 1700 may be a general-purpose alternating-current(AC) electric power supply, or power supply interface, such as aninterface to a household power source. In some implementations, thepower source 1700 may be a single use battery or a rechargeable batteryto allow the computing device 1000 to operate independently of anexternal power distribution system. For example, the power source 1700may include a wired power source; one or more dry cell batteries, suchas nickel-cadmium (NiCad), nickel-zinc (NiZn), nickel metal hydride(NiMH), lithium-ion (Li-ion); solar cells; fuel cells; or any otherdevice capable of powering the computing device 1000.

FIG. 2 is a block diagram of an example of a computing system 2000. Asshown, the computing system 2000 includes an external data sourceportion 2100, an internal database analysis portion 2200, and a systeminterface portion 2300. The computing system 2000 may include otherelements not shown in FIG. 2, such as computer network elements.

The external data source portion 2100 may be associated with, such ascontrolled by, an external person, entity, or organization(second-party). The internal database analysis portion 2200 may beassociated with, such as created by or controlled by, a person, entity,or organization (first-party). The system interface portion 2300 may beassociated with, such as created by or controlled by, the first-partyand may be accessed by the first-party, the second-party, third-parties,or a combination thereof, such as in accordance with access andauthorization permissions and procedures.

The external data source portion 2100 is shown as including externaldatabase servers 2120 and external application servers 2140. Theexternal data source portion 2100 may include other elements not shownin FIG. 2. The external data source portion 2100 may include externalcomputing devices, such as the computing device 1000 shown in FIG. 1,which may be used by or accessible to the external person, entity, ororganization (second-party) associated with the external data sourceportion 2100, including but not limited to external database servers2120 and external application servers 2140. The external computingdevices may include data regarding the operation of the external person,entity, or organization (second-party) associated with the external datasource portion 2100.

The external database servers 2120 may be one or more computing devicesconfigured to store data in a format and schema determined externallyfrom the internal database analysis portion 2200, such as by asecond-party associated with the external data source portion 2100, or athird party. For example, the external database server 2120 may use arelational database and may include a database catalog with a schema. Insome embodiments, the external database server 2120 may include anon-database data storage structure, such as a text-based datastructure, such as a comma separated variable structure or an extensiblemarkup language formatted structure or file. For example, the externaldatabase servers 2120 can include data regarding the production ofmaterials by the external person, entity, or organization (second-party)associated with the external data source portion 2100, communicationsbetween the external person, entity, or organization (second-party)associated with the external data source portion 2100 and third parties,or a combination thereof. Other data may be included. The externaldatabase may be a structured database system, such as a relationaldatabase operating in a relational database management system (RDBMS),which may be an enterprise database. In some embodiments, the externaldatabase may be an unstructured data source. The external data mayinclude data or content, such as sales data, revenue data, profit data,tax data, shipping data, safety data, sports data, health data, weatherdata, or the like, or any other data, or combination of data, that maybe generated by or associated with a user, an organization, or anenterprise and stored in a database system. For simplicity and clarity,data stored in or received from the external data source portion 2100may be referred to herein as enterprise data.

The external application server 2140 may include application software,such as application software used by the external person, entity, ororganization (second-party) associated with the external data sourceportion 2100. The external application server 2140 may include data ormetadata relating to the application software.

The external database servers 2120, the external application servers2140, or both, shown in FIG. 2 may represent logical units or devicesthat may be implemented on one or more physical units or devices, whichmay be controlled or operated by the first party, the second party, or athird party.

The external data source portion 2100, or aspects thereof, such as theexternal database servers 2120, the external application servers 2140,or both, may communicate with the internal database analysis portion2200, or an aspect thereof, such as one or more of the servers 2220,2240, 2260, and 2280, via an electronic communication medium, which maybe a wired or wireless electronic communication medium. For example, theelectronic communication medium may include a local area network (LAN),a wide area network (WAN), a fiber channel network, the Internet, or acombination thereof.

The internal database analysis portion 2200 is shown as includingservers 2220, 2240, 2260, and 2280. The servers 2220, 2240, 2260, and2280 may be computing devices, such as the computing device 1000 shownin FIG. 1. Although four servers 2220, 2240, 2260, and 2280 are shown inFIG. 2, other numbers, or cardinalities, of servers may be used. Forexample, the number of computing devices may be determined based on thecapability of individual computing devices, the amount of data to beprocessed, the complexity of the data to be processed, or a combinationthereof. Other metrics may be used for determining the number ofcomputing devices.

The internal database analysis portion 2200 may store data, processdata, or store and process data. The internal database analysis portion2200 may include a distributed cluster (not expressly shown) which mayinclude two or more of the servers 2220, 2240, 2260, and 2280. Theoperation of distributed cluster, such as the operation of the servers2220, 2240, 2260, and 2280 individually, in combination, or both, may bemanaged by a distributed cluster manager. For example, the server 2220may be the distributed cluster manager. In another example, thedistributed cluster manager may be implemented on another computingdevice (not shown). The data and processing of the distributed clustermay be distributed among the servers 2220, 2240, 2260, and 2280, such asby the distributed cluster manager.

Enterprise data from the external data source portion 2100, such as fromthe external database server 2120, the external application server 2140,or both may be imported into the internal database analysis portion2200. The external database server 2120, the external application server2140, or both may be one or more computing devices and may communicatewith the internal database analysis portion 2200 via electroniccommunication. The imported data may be distributed among, processed by,stored on, or a combination thereof, one or more of the servers 2220,2240, 2260, and 2280. Importing the enterprise data may includeimporting or accessing the data structures of the enterprise data.Importing the enterprise data may include generating internal data,internal data structures, or both, based on the enterprise data. Theinternal data, internal data structures, or both may accuratelyrepresent and may differ from the enterprise data, the data structuresof the enterprise data, or both. In some implementations, enterprisedata from multiple external data sources may be imported into theinternal database analysis portion 2200. For simplicity and clarity,data stored or used in the internal database analysis portion 2200 maybe referred to herein as internal data. For example, the internal data,or a portion thereof, may represent, and may be distinct from,enterprise data imported into or accessed by the internal databaseanalysis portion 2200.

The system interface portion 2300 may include one or more client devices2320, 2340. The client devices 2320, 2340 may be computing devices, suchas the computing device 1000 shown in FIG. 1. For example, one of theclient devices 2320, 2340 may be a desktop or laptop computer and theother of the client devices 2320, 2340 may be a mobile device,smartphone, or tablet. One or more of the client devices 2320, 2340 mayaccess the internal database analysis portion 2200. For example, theinternal database analysis portion 2200 may provide one or moreservices, application interfaces, or other electronic computercommunication interfaces, such as a web site, and the client devices2320, 2340 may access the interfaces provided by the internal databaseanalysis portion 2200, which may include accessing the internal datastored in the internal database analysis portion 2200.

In an example, one or more of the client devices 2320, 2340 may send amessage or signal indicating a request for data, which may include arequest for data analysis, to the internal database analysis portion2200. The internal database analysis portion 2200 may receive andprocess the request, which may include distributing the processing amongone or more of the servers 2220, 2240, 2260, and 2280, may generate aresponse to the request, which may include generating or modifyinginternal data, internal data structures, or both, and may output theresponse to the client device 2320, 2340 that sent the request.Processing the request may include accessing one or more internal dataindexes, an internal database, or a combination thereof. The clientdevice 2320, 2340 may receive the response, including the response dataor a portion thereof, and may store, output, or both, the response or arepresentation thereof, such as a representation of the response data,or a portion thereof, which may include presenting the representationvia a user interface on a presentation device of the client device 2320,2340, such as to a user of the client device 2320, 2340.

The system interface portion 2300, or aspects thereof, such as one ormore of the client devices 2320, 2340, may communicate with the internaldatabase analysis portion 2200, or an aspect thereof, such as one ormore of the servers 2220, 2240, 2260, and 2280, via an electroniccommunication medium, which may be a wired or wireless electroniccommunication medium. For example, the electronic communication mediummay include a local area network (LAN), a wide area network (WAN), afiber channel network, the Internet, or a combination thereof.

FIG. 3 is a block diagram of an example of a low-latency databaseanalysis system 3000. The low-latency database analysis system 3000, oraspects thereof, may be similar to the internal database analysisportion 2200 shown in FIG.2, except as described herein or otherwiseclear from context. The low-latency database analysis system 3000, oraspects thereof, may be implemented on one or more computing devices,such as servers 2220, 2240, 2260, and 2280 shown in FIG. 2, which may bein a clustered or distributed computing configuration.

The low-latency database analysis system 3000 may store and maintain theinternal data, or a portion thereof, such as low-latency data, in alow-latency memory device, such as the low-latency memory 1300 shown inFIG. 1, or any other type of data storage medium or combination of datastorage devices with relatively fast (low-latency) data access,organized in a low-latency data structure. In some embodiments, thelow-latency database analysis system 3000 may be implemented as one ormore logical devices in a cloud-based configuration optimized forautomatic database analysis.

As shown, the low-latency database analysis system 3000 includes adistributed cluster manager 3100, a security and governance unit 3200, adistributed in-memory database 3300, an enterprise data interface unit3400, a distributed in-memory ontology unit 3500, a semantic interfaceunit 3600, a relational search unit 3700, a natural language processingunit 3710, a data utility unit 3720, an insight unit 3730, an objectsearch unit 3800, an object utility unit 3810, a system configurationunit 3820, a user customization unit 3830, a system access interfaceunit 3900, a real-time collaboration unit 3910, a third-partyintegration unit 3920, and a persistent storage unit 3930, which may becollectively referred to as the components of the low-latency databaseanalysis system 3000.

Although not expressly shown in FIG. 3, one or more of the components ofthe low-latency database analysis system 3000 may be implemented on oneor more operatively connected physical or logical computing devices,such as in a distributed cluster computing configuration, such as theinternal database analysis portion 2200 shown in FIG. 2. Although shownseparately in FIG. 3, one or more of the components of the low-latencydatabase analysis system 3000, or respective aspects thereof, may becombined or otherwise organized.

The low-latency database analysis system 3000 may include different,fewer, or additional components not shown in FIG. 3. The aspects orcomponents implemented in an instance of the low-latency databaseanalysis system 3000 may be configurable. For example, the insight unit3730 may be omitted or disabled. One or more of the components of thelow-latency database analysis system 3000 may be implemented in a mannersuch that aspects thereof are divided or combined into variousexecutable modules or libraries in a manner which may differ from thatdescribed herein.

The low-latency database analysis system 3000 may implement anapplication programming interface (API), which may monitor, receive, orboth, input signals or messages from external devices and systems,client systems, process received signals or messages, transmitcorresponding signals or messages to one or more of the components ofthe low-latency database analysis system 3000, and output, such astransmit or send, output messages or signals to respective externaldevices or systems. The low-latency database analysis system 3000 may beimplemented in a distributed computing configuration.

The distributed cluster manager 3100 manages the operative configurationof the low-latency database analysis system 3000. Managing the operativeconfiguration of the low-latency database analysis system 3000 mayinclude controlling the implementation of and distribution of processingand storage across one or more logical devices operating on one or morephysical devices, such as the servers 2220, 2240, 2260, and 2280 shownin FIG. 2. The distributed cluster manager 3100 may generate andmaintain configuration data for the low-latency database analysis system3000, such as in one or more tables, identifying the operativeconfiguration of the low-latency database analysis system 3000. Forexample, the distributed cluster manager 3100 may automatically updatethe low-latency database analysis system configuration data in responseto an operative configuration event, such as a change in availability orperformance for a physical or logical unit of the low-latency databaseanalysis system 3000. One or more of the component units of low-latencydatabase analysis system 3000 may access the database analysis systemconfiguration data, such as to identify intercommunication parameters orpaths.

The security and governance unit 3200 may describe, implement, enforce,or a combination thereof, rules and procedures for controlling access toaspects of the low-latency database analysis system 3000, such as theinternal data of the low-latency database analysis system 3000 and thefeatures and interfaces of the low-latency database analysis system3000. The security and governance unit 3200 may apply security at anontological level to control or limit access to the internal data of thelow-latency database analysis system 3000, such as to columns, tables,rows, or fields, which may include using row level security.

Although shown as a single unit in FIG. 3, the distributed in-memorydatabase 3300 may be implemented in a distributed configuration, such asdistributed among the servers 2220, 2240, 2260, and 2280 shown in FIG.2, which may include multiple in-memory database instances. Eachin-memory database instance may utilize one or more distinct resources,such as processing or low-latency memory resources, that differ from theresources utilized by the other in-memory database instances. In someembodiments, the in-memory database instances may utilize one or moreshared resources, such as resources utilized by two or more in-memorydatabase instances.

The distributed in-memory database 3300 may generate, maintain, or both,a low-latency data structure and data stored or maintained therein(low-latency data). The low-latency data may include principal data,which may represent enterprise data, such as enterprise data importedfrom an external enterprise data source, such as the external datasource portion 2100 shown in FIG. 2. In some implementations, thedistributed in-memory database 3300 may include system internal datarepresenting one or more aspects, features, or configurations of thelow-latency database analysis system 3000. The distributed in-memorydatabase 3300 and the low-latency data stored therein, or a portionthereof, may be accessed using commands, messages, or signals inaccordance with a defined structured query language associated with thedistributed in-memory database 3300.

The low-latency data, or a portion thereof, may be organized as tablesin the distributed in-memory database 3300. A table may be a datastructure to organize or group the data or a portion thereof, such asrelated or similar data. A table may have a defined structure. Forexample, each table may define or describe a respective set of one ormore columns.

A column may define or describe the characteristics of a discrete aspectof the data in the table. For example, the definition or description ofa column may include an identifier, such as a name, for the columnwithin the table, and one or more constraints, such as a data type, forthe data corresponding to the column in the table. The definition ordescription of a column may include other information, such as adescription of the column. The data in a table may be accessible orpartitionable on a per-column basis. The set of tables, including thecolumn definitions therein, and information describing relationshipsbetween elements, such as tables and columns, of the database may bedefined or described by a database schema or design. The cardinality ofcolumns of a table, and the definition and organization of the columns,may be defined by the database schema or design. Adding, deleting, ormodifying a table, a column, the definition thereof, or a relationshipor constraint thereon, may be a modification of the database design,schema, model, or structure.

The low-latency data, or a portion thereof, may be stored in thedatabase as one or more rows or records in respective tables. Eachrecord or row of a table may include a respective field or cellcorresponding to each column of the table. A field may store a discretedata value. The cardinality of rows of a table, and the values storedtherein, may be variable based on the data. Adding, deleting, ormodifying rows, or the data stored therein may omit modification of thedatabase design, schema, or structure. The data stored in respectivecolumns may be identified or defined as a measure data, attribute data,or enterprise ontology data (e.g., metadata).

Measure data, or measure values, may include quantifiable or additivenumeric values, such as integer or floating-point values, which mayinclude numeric values indicating sizes, amounts, degrees, or the like.A column defined as representing measure values may be referred toherein as a measure or fact. A measure may be a property on whichquantitative operations (e.g., sum, count, average, minimum, maximum)may be performed to calculate or determine a result or output.

Attribute data, or attribute values, may include non-quantifiablevalues, such as text or image data, which may indicate names anddescriptions, quantifiable values designated, defined, or identified asattribute data, such as numeric unit identifiers, or a combinationthereof. A column defined as including attribute values may be referredto herein as an attribute or dimension. For example, attributes mayinclude text, identifiers, timestamps, or the like.

Enterprise ontology data may include data that defines or describes oneor more aspects of the database, such as data that describes one or moreaspects of the attributes, measures, rows, columns, tables,relationships, or other aspects of the data or database schema. Forexample, a portion of the database design, model, or schema may berepresented as enterprise ontology data in one or more tables in thedatabase.

Distinctly identifiable data in the low-latency data may be referred toherein as a data portion. For example, the low-latency data stored inthe distributed in-memory database 3300 may be referred to herein as adata portion, a table from the low-latency data may be referred toherein as a data portion, a column from the low-latency data may bereferred to herein as a data portion, a row or record from thelow-latency data may be referred to herein as a data portion, a valuefrom the low-latency data may be referred to herein as a data portion, arelationship defined in the low-latency data may be referred to hereinas a data portion, enterprise ontology data describing the low-latencydata may be referred to herein as a data portion, or any otherdistinctly identifiable data, or combination thereof, from thelow-latency data may be referred to herein as a data portion.

The distributed in-memory database 3300 may create or add one or moredata portions, such as a table, may read from or access one or more dataportions, may update or modify one or more data portions, may remove ordelete one or more data portions, or a combination thereof. Adding,modifying, or removing data portions may include changes to the datamodel of the low-latency data. Changing the data model of thelow-latency data may include notifying one or more other components ofthe low-latency database analysis system 3000, such as by sending, orotherwise making available, a message or signal indicating the change.For example, the distributed in-memory database 3300 may create or add atable to the low-latency data and may transmit or send a message orsignal indicating the change to the semantic interface unit 3600.

In some implementations, a portion of the low-latency data may representa data model of an external enterprise database and may omit the datastored in the external enterprise database, or a portion thereof. Forexample, prioritized data may be cached in the distributed in-memorydatabase 3300 and the other data may be omitted from storage in thedistributed in-memory database 3300, which may be stored in the externalenterprise database. In some implementations, requesting data from thedistributed in-memory database 3300 may include requesting the data, ora portion thereof, from the external enterprise database.

The distributed in-memory database 3300 may receive one or more messagesor signals indicating respective data-queries for the low-latency data,or a portion thereof, which may include data-queries for modified,generated, or aggregated data generated based on the low-latency data,or a portion thereof. For example, the distributed in-memory database3300 may receive a data-query from the semantic interface unit 3600,such as in accordance with a request for data. The data-queries receivedby the distributed in-memory database 3300 may be agnostic to thedistributed configuration of the distributed in-memory database 3300. Adata-query, or a portion thereof, may be expressed in accordance withthe defined structured query language implemented by the distributedin-memory database 3300. In some implementations, a data-query may beincluded, such as stored or communicated, in a data-query data structureor container.

The distributed in-memory database 3300 may execute or perform one ormore queries to generate or obtain response data responsive to thedata-query based on the low-latency data.

The distributed in-memory database 3300 may interpret, evaluate, orotherwise process a data-query to generate one or moredistributed-queries, which maybe expressed in accordance with thedefined structured query language. For example, an in-memory databaseinstance of the distributed in-memory database 3300 may be identified asa query coordinator. The query coordinator may generate a query plan,which may include generating one or more distributed-queries, based onthe received data-query. The query plan may include query executioninstructions for executing one or more queries, or one or more portionsthereof, based on the received data-query by the one or more of thein-memory database instances. Generating the query plan may includeoptimizing the query plan. The query coordinator may distribute, orotherwise make available, the respective portions of the query plan, asquery execution instructions, to the corresponding in-memory databaseinstances.

The respective in-memory database instances may receive thecorresponding query execution instructions from the query coordinator.The respective in-memory database instances may execute thecorresponding query execution instructions to obtain, process, or both,data (intermediate results data) from the low-latency data. Therespective in-memory database instances may output, or otherwise makeavailable, the intermediate results data, such as to the querycoordinator.

The query coordinator may execute a respective portion of queryexecution instructions (allocated to the query coordinator) to obtain,process, or both, data (intermediate results data) from the low-latencydata. The query coordinator may receive, or otherwise access, theintermediate results data from the respective in-memory databaseinstances. The query coordinator may combine, aggregate, or otherwiseprocess, the intermediate results data to obtain results data.

In some embodiments, obtaining the intermediate results data by one ormore of the in-memory database instances may include outputting theintermediate results data to, or obtaining intermediate results datafrom, one or more other in-memory database instances, in addition to, orinstead of, obtaining the intermediate results data from the low-latencydata.

The distributed in-memory database 3300 may output, or otherwise makeavailable, the results data to the semantic interface unit 3600.

The enterprise data interface unit 3400 may interface with, orcommunicate with, an external enterprise data system. For example, theenterprise data interface unit 3400 may receive or access enterprisedata from or in an external system, such as an external database. Theenterprise data interface unit 3400 may import, evaluate, or otherwiseprocess the enterprise data to populate, create, or modify data storedin the low-latency database analysis system 3000. The enterprise datainterface unit 3400 may receive, or otherwise access, the enterprisedata from one or more external data sources, such as the external datasource portion 2100 shown in FIG. 2, and may represent the enterprisedata in the low-latency database analysis system 3000 by importing,loading, or populating the enterprise data as principal data in thedistributed in-memory database 3300, such as in one or more low-latencydata structures. The enterprise data interface unit 3400 may implementone or more data connectors, which may transfer data between, forexample, the external data source and the distributed in-memory database3300, which may include altering, formatting, evaluating, ormanipulating the data.

The enterprise data interface unit 3400 may receive, access, or generatemetadata that identifies one or more parameters or relationships for theprincipal data, such as based on the enterprise data, and may includethe generated metadata in the low-latency data stored in the distributedin-memory database 3300. For example, the enterprise data interface unit3400 may identify characteristics of the principal data such as,attributes, measures, values, unique identifiers, tags, links, keys, orthe like, and may include metadata representing the identifiedcharacteristics in the low-latency data stored in the distributedin-memory database 3300. The characteristics of the data can beautomatically determined by receiving, accessing, processing,evaluating, or interpreting the schema in which the enterprise data isstored, which may include automatically identifying links orrelationships between columns, classifying columns (e.g., using columnnames), and analyzing or evaluating the data.

Distinctly identifiable operative data units or structures representingone or more data portions, one or more entities, users, groups, ororganizations represented in the internal data, or one or moreaggregations, collections, relations, analytical results,visualizations, or groupings thereof, may be represented in thelow-latency database analysis system 3000 as objects. An object mayinclude a unique identifier for the object, such as a fully qualifiedname. An object may include a name, such as a displayable value, for theobject.

For example, an object may represent a user, a group, an entity, anorganization, a privilege, a role, a table, a column, a datarelationship, a worksheet, a view, a context, an answer, an insight, apinboard, a tag, a comment, a trigger, a defined variable, a datasource, an object-level security rule, a row-level security rule, or anyother data capable of being distinctly identified and stored orotherwise obtained in the low-latency database analysis system 3000. Anobject may represent or correspond with a logical entity. Datadescribing an object may include data operatively or uniquelyidentifying data corresponding to, or represented by, the object in thelow-latency database analysis system. For example, a column in a tablein a database in the low-latency database analysis system may berepresented in the low-latency database analysis system as an object andthe data describing or defining the object may include data operativelyor uniquely identifying the column.

A worksheet (worksheet object), or worksheet table, may be a logicaltable, or a definition thereof, which may be a collection, a sub-set(such as a subset of columns from one or more tables), or both, of datafrom one or more data sources, such as columns in one or more tables,such as in the distributed in-memory database 3300. A worksheet, or adefinition thereof, may include one or more data organization ormanipulation definitions, such as join paths or worksheet-columndefinitions, which may be user defined. A worksheet may be a datastructure that may contain one or more rules or definitions that maydefine or describe how a respective tabular set of data may be obtained,which may include defining one or more sources of data, such as one ormore columns from the distributed in-memory database 3300. A worksheetmay be a data source. For example, a worksheet may include references toone or more data sources, such as columns in one or more tables, such asin the distributed in-memory database 3300, and a request for datareferencing the worksheet may access the data from the data sourcesreferenced in the worksheet. In some implementations, a worksheet mayomit aggregations of the data from the data sources referenced in theworksheet.

An answer (answer object), or report, may be a defined, such aspreviously generated, request for data, such as a resolved-request. Ananswer may include information describing a visualization of dataresponsive to the request for data.

A view (view object) may be a logical table, or a definition thereof,which may be a collection, a sub-set, or both, of data from one or moredata sources, such as columns in one or more tables, such as in thedistributed in-memory database 3300. For example, a view may begenerated based on an answer, such as by storing the answer as a view. Aview may define or describe a data aggregation. A view may be a datasource. For example, a view may include references to one or more datasources, such as columns in one or more tables, such as in thedistributed in-memory database 3300, which may include a definition ordescription of an aggregation of the data from a respective data source,and a request for data referencing the view may access the aggregateddata, the data from the unaggregated data sources referenced in theworksheet, or a combination thereof. The unaggregated data from datasources referenced in the view defined or described as aggregated datain the view may be unavailable based on the view. A view may be amaterialized view or an unmaterialized view. A request for datareferencing a materialized view may obtain data from a set of datapreviously obtained (view-materialization) in accordance with thedefinition of the view and the request for data. A request for datareferencing an unmaterialized view may obtain data from a set of datacurrently obtained in accordance with the definition of the view and therequest for data.

A pinboard (pinboard object), or dashboard, may be a defined collectionor grouping of objects, such as visualizations, answers, or insights.Pinboard data for a pinboard may include information associated with thepinboard, which may be associated with respective objects included inthe pinboard.

A context (context object) may be a set or collection of data associatedwith a request for data or a discretely related sequence or series ofrequests for data or other interactions with the low-latency databaseanalysis system 3000.

A definition may be a set of data describing the structure ororganization of a data portion. For example, in the distributedin-memory database 3300, a column definition may define one or moreaspects of a column in a table, such as a name of the column, adescription of the column, a datatype for the column, or any otherinformation about the column that may be represented as discrete data.

A data source object may represent a source or repository of dataaccessible by the low-latency database analysis system 3000. A datasource object may include data indicating an electronic communicationlocation, such as an address, of a data source, connection information,such as protocol information, authentication information, or acombination thereof, or any other information about the data source thatmay be represented as discrete data. For example, a data source objectmay represent a table in the distributed in-memory database 3300 andinclude data for accessing the table from the database, such asinformation identifying the database, information identifying a schemawithin the database, and information identifying the table within theschema within the database. An external data source object may representan external data source. For example, an external data source object mayinclude data indicating an electronic communication location, such as anaddress, of an external data source, connection information, such asprotocol information, authentication information, or a combinationthereof, or any other information about the external data source thatmay be represented as discrete data.

A sticker (sticker object) may be a description of a classification,category, tag, subject area, or other information that may be associatedwith one or more other objects such that objects associated with asticker may be grouped, sorted, filtered, or otherwise identified basedon the sticker. In the distributed in-memory database 3300 a tag may bea discrete data portion that may be associated with other data portions,such that data portions associated with a tag may be grouped, sorted,filtered, or otherwise identified based on the tag.

The distributed in-memory ontology unit 3500 generates, maintains, orboth, information (ontological data) defining or describing theoperative ontological structure of the objects represented in thelow-latency database analysis system 3000, such as in the low-latencydata stored in the distributed in-memory database 3300, which mayinclude describing attributes, properties, states, or other informationabout respective objects and may include describing relationships amongrespective objects.

Objects may be referred to herein as primary objects, secondary objects,or tertiary objects. Other types of objects may be used.

Primary objects may include objects representing distinctly identifiableoperative data units or structures representing one or more dataportions in the distributed in-memory database 3300, or another datasource in the low-latency database analysis system 3000. For example,primary objects may be data source objects, table objects, columnobjects, relationship objects, or the like. Primary objects may includeworksheets, views, filters, such as row-level-security filters and tablefilters, variables, or the like. Primary objects may be referred toherein as data-objects or queryable-objects.

Secondary objects may be objects representing distinctly identifiableoperative data units or structures representing analytical dataaggregations, collections, analytical results, visualizations, orgroupings thereof, such as pinboard objects, answer objects, insights,visualization objects, and the like. Secondary objects may be referredto herein as analytical-objects.

Tertiary objects may be objects representing distinctly identifiableoperative data units or structures representing operational aspects ofthe low-latency database analysis system 3000, such as one or moreentities, users, groups, or organizations represented in the internaldata, such as user objects, user-group objects, role objects, stickerobjects, and the like.

The distributed in-memory ontology unit 3500 may represent theontological structure, which may include the objects therein, as a graphhaving nodes and edges. A node may be a representation of an object inthe graph structure of the distributed in-memory ontology unit 3500. Anode object can include one or more component objects. Component objectsmay be versioned, such as on a per-component object basis. For example,a node can include a header object, a content object, or both. A headerobject may include information about the node. A content may include thecontent of the node. An edge may represent a relationship between nodes,which may be directional.

In some implementations, the distributed in-memory ontology unit 3500graph may include one or more nodes, edges, or both, representing one ormore objects, relationships or both, corresponding to a respectiveinternal representation of enterprise data stored in an externalenterprise data storage unit, wherein a portion of the data stored inthe external enterprise data storage unit represented in the distributedin-memory ontology unit 3500 graph is omitted from the distributedin-memory database 3300.

In some embodiments, the distributed in-memory ontology unit 3500 maygenerate, modify, or remove a portion of the ontology graph in responseto one or more messages, signals, or notifications from one or more ofthe components of the low-latency database analysis system 3000. Forexample, the distributed in-memory ontology unit 3500 may generate,modify, or remove a portion of the ontology graph in response toreceiving one or more messages, signals, or notifications from thedistributed in-memory database 3300 indicating a change to thelow-latency data structure. In another example, the distributedin-memory database 3300 may send one or more messages, signals, ornotifications indicating a change to the low-latency data structure tothe semantic interface unit 3600 and the semantic interface unit 3600may send one or more messages, signals, or notifications indicating thechange to the low-latency data structure to the distributed in-memoryontology unit 3500.

The distributed in-memory ontology unit 3500 may be distributed,in-memory, multi-versioned, transactional, consistent, durable, or acombination thereof. The distributed in-memory ontology unit 3500 istransactional, which may include implementing atomic concurrent, orsubstantially concurrent, updating of multiple objects. The distributedin-memory ontology unit 3500 is durable, which may include implementinga robust storage that prevents data loss subsequent to or as a result ofthe completion of an atomic operation. The distributed in-memoryontology unit 3500 is consistent, which may include performingoperations associated with a request for data with reference to or usinga discrete data set, which may mitigate or eliminate the riskinconsistent results.

The distributed in-memory ontology unit 3500 may generate, output, orboth, one or more event notifications. For example, the distributedin-memory ontology unit 3500 may generate, output, or both, anotification, or notifications, in response to a change of thedistributed in-memory ontology. The distributed in-memory ontology unit3500 may identify a portion of the distributed in-memory ontology(graph) associated with a change of the distributed in-memory ontology,such as one or more nodes depending from a changed node, and maygenerate, output, or both, a notification, or notifications indicatingthe identified relevant portion of the distributed in-memory ontology(graph). One or more aspects of the low-latency database analysis system3000 may cache object data and may receive the notifications from thedistributed in-memory ontology unit 3500, which may reduce latency andnetwork traffic relative to systems that omit caching object data oromit notifications relevant to changes to portions of the distributedin-memory ontology (graph).

The distributed in-memory ontology unit 3500 may implement prefetching.For example, the distributed in-memory ontology unit 3500 maypredictively, such as based on determined probabilistic utility, fetchone or more nodes, such as in response to access to a related node by acomponent of the low-latency database analysis system 3000.

The distributed in-memory ontology unit 3500 may implement amulti-version concurrency control graph data storage unit. Each node,object, or both, may be versioned. Changes to the distributed in-memoryontology may be reversible. For example, the distributed in-memoryontology may have a first state prior to a change to the distributedin-memory ontology, the distributed in-memory ontology may have a secondstate subsequent to the change, and the state of the distributedin-memory ontology may be reverted to the first state subsequent to thechange, such as in response to the identification of an error or failureassociated with the second state.

In some implementations, reverting a node, or a set of nodes, may omitreverting one or more other nodes. In some implementations, thedistributed in-memory ontology unit 3500 may maintain a change logindicating a sequential record of changes to the distributed in-memoryontology (graph), such that a change to a node or a set of nodes may bereverted and one or more other changes subsequent to the reverted changemay be reverted for consistency.

The distributed in-memory ontology unit 3500 may implement optimisticlocking to reduce lock contention times. The use of optimistic lockingpermits improved throughput of data through the distributed in-memoryontology unit 3500.

The semantic interface unit 3600 may implement procedures and functionsto provide a semantic interface between the distributed in-memorydatabase 3300 and one or more of the other components of the low-latencydatabase analysis system 3000.

The semantic interface unit 3600 may implement ontological datamanagement, data-query generation, authentication and access control,object statistical data collection, or a combination thereof.

Ontological data management may include object lifecycle management,object data persistence, ontological modifications, or the like. Objectlifecycle management may include creating one or more objects, readingor otherwise accessing one or more objects, updating or modifying one ormore objects, deleting or removing one or more objects, or a combinationthereof. For example, the semantic interface unit 3600 may interface orcommunicate with the distributed in-memory ontology unit 3500, which maystore the ontological data, object data, or both, to perform objectlifecycle management, object data persistence, ontologicalmodifications, or the like.

For example, the semantic interface unit 3600 may receive, or otherwiseaccess, a message, signal, or notification, such as from the distributedin-memory database 3300, indicating the creation or addition of a dataportion, such as a table, in the low-latency data stored in thedistributed in-memory database 3300, and the semantic interface unit3600 may communicate with the distributed in-memory ontology unit 3500to create an object in the ontology representing the added data portion.The semantic interface unit 3600 may transmit, send, or otherwise makeavailable, a notification, message, or signal to the relational searchunit 3700 indicating that the ontology has changed.

The semantic interface unit 3600 may receive, or otherwise access, arequest message or signal, such as from the relational search unit 3700,indicating a request for information describing changes to the ontology(ontological updates request). The semantic interface unit 3600 maygenerate and send, or otherwise make available, a response message orsignal to the relational search unit 3700 indicating the changes to theontology (ontological updates response). The semantic interface unit3600 may identify one or more data portions for indexing based on thechanges to the ontology. For example, the changes to the ontology mayinclude adding a table to the ontology, the table including multiplerows, and the semantic interface unit 3600 may identify each row as adata portion for indexing. The semantic interface unit 3600 may includeinformation describing the ontological changes in the ontologicalupdates response. The semantic interface unit 3600 may include one ormore data-query definitions, such as data-query definitions for indexingdata-queries, for each data portion identified for indexing in theontological updates response. For example, the data-query definitionsmay include a sampling data-query, which may be used to query thedistributed in-memory database 3300 for sample data from the added dataportion, an indexing data-query, which may be used to query thedistributed in-memory database 3300 for data from the added dataportion, or both.

The semantic interface unit 3600 may receive, or otherwise access,internal signals or messages including data expressing a usage intent,such as data indicating requests to access or modify the low-latencydata stored in the distributed in-memory database 3300 (e.g., a requestfor data). The request to access or modify the low-latency data receivedby the semantic interface unit 3600 may include a resolved-request. Theresolved-request, which may be database and visualization agnostic, maybe expressed or communicated as an ordered sequence of tokens, which mayrepresent semantic data. For example, the relational search unit 3700may tokenize, identify semantics, or both, based on input data, such asinput data representing user input, to generate the resolved-request.The resolved-request may include an ordered sequence of tokens thatrepresent the request for data corresponding to the input data, and maytransmit, send, or otherwise make accessible, the resolved-request tothe semantic interface unit 3600. The semantic interface unit 3600 mayprocess or respond to a received resolved-request.

The semantic interface unit 3600 may process or transform the receivedresolved-request, which may be, at least in part, incompatible with thedistributed in-memory database 3300, to generate one or morecorresponding data-queries that are compatible with the distributedin-memory database 3300, which may include generating a proto-queryrepresenting the resolved-request, generating a pseudo-queryrepresenting the proto-query, and generating the data-query representingthe pseudo-query.

The semantic interface unit 3600 may generate a proto-query based on theresolved-request. A proto-query, which may be database agnostic, may bestructured or formatted in a form, language, or protocol that differsfrom the defined structured query language of the distributed in-memorydatabase 3300. Generating the proto-query may include identifyingvisualization identification data, such as an indication of a type ofvisualization, associated with the request for data, and generating theproto-query based on the resolved-request and the visualizationidentification data.

The semantic interface unit 3600 may transform the proto-query togenerate a pseudo-query. The pseudo-query, which may be databaseagnostic, may be structured or formatted in a form, language, orprotocol that differs from the defined structured query language of thedistributed in-memory database 3300. Generating a pseudo-query mayinclude applying a defined transformation, or an ordered sequence oftransformations. Generating a pseudo-query may include incorporatingrow-level security filters in the pseudo-query.

The semantic interface unit 3600 may generate a data-query based on thepseudo-query, such as by serializing the pseudo-query. The data-query,or a portion thereof, may be structured or formatted using the definedstructured query language of the distributed in-memory database 3300. Insome implementations, a data-query may be structured or formatted usinga defined structured query language of another database, which maydiffer from the defined structured query language of the distributedin-memory database 3300. Generating the data-query may include using oneor more defined rules for expressing respective the structure andcontent of a pseudo-query in the respective defined structured querylanguage.

The semantic interface unit 3600 may communicate, or issue, thedata-query to the distributed in-memory database 3300. In someimplementations, processing or responding to a resolved-request mayinclude generating and issuing multiple data-queries to the distributedin-memory database 3300.

The semantic interface unit 3600 may receive results data from thedistributed in-memory database 3300 responsive to one or moreresolved-requests. The semantic interface unit 3600 may process, format,or transform the results data to obtain visualization data. For example,the semantic interface unit 3600 may identify a visualization forrepresenting or presenting the results data, or a portion thereof, suchas based on the results data or a portion thereof. For example, thesemantic interface unit 3600 may identifying a bar chart visualizationfor results data including one measure and attribute.

Although not shown separately in FIG. 3, the semantic interface unit3600 may include a data visualization unit. In some embodiments, thedata visualization unit may be a distinct unit, separate from thesemantic interface unit 3600. In some embodiments, the datavisualization unit may be included in the system access interface unit3900. The data visualization unit, the system access interface unit3900, or a combination thereof, may generate a user interface, or one ormore portions thereof. For example, data visualization unit, the systemaccess interface unit 3900, or a combination thereof, may obtain theresults data, such as the visualization data, and may generate userinterface elements (visualizations) representing the results data.

The semantic interface unit 3600 may implement object-level security,row-level security, or a combination thereof. Object level security mayinclude security associated with an object, such as a table, a column, aworksheet, an answer, or a pinboard. Row-level security may includeuser-based or group-based access control of rows of data in thelow-latency data, the indexes, or both. The semantic interface unit 3600may implement on or more authentication procedures, access controlprocedures, or a combination thereof.

The semantic interface unit 3600 may implement one or more user-dataintegration features. For example, the semantic interface unit 3600 maygenerate and output a user interface, or a portion thereof, forinputting, uploading, or importing user data, may receive user data, andmay import the user data. For example, the user data may be enterprisedata.

The semantic interface unit 3600 may implement object statistical datacollection. Object statistical data may include, for respective objects,temporal access information, access frequency information, accessrecency information, access requester information, or the like. Forexample, the semantic interface unit 3600 may obtain object statisticaldata as described with respect to the data utility unit 3720, the objectutility unit 3810, or both. The semantic interface unit 3600 may send,transmit, or otherwise make available, the object statistical data fordata-objects to the data utility unit 3720. The semantic interface unit3600 may send, transmit, or otherwise make available, the objectstatistical data for analytical-objects to the object utility unit 3810.

The semantic interface unit 3600 may implement or expose one or moreservices or application programming interfaces. For example, thesemantic interface unit 3600 may implement one or more services foraccess by the system access interface unit 3900. In someimplementations, one or more services or application programminginterfaces may be exposed to one or more external devices or systems.

The semantic interface unit 3600 may generate and transmit, send, orotherwise communicate, one or more external communications, such ase-mail messages, such as periodically, in response to one or moreevents, or both. For example, the semantic interface unit 3600 maygenerate and transmit, send, or otherwise communicate, one or moreexternal communications including a portable representation, such as aportable document format representation of one or more pinboards inaccordance with a defined schedule, period, or interval. In anotherexample, the semantic interface unit 3600 may generate and transmit,send, or otherwise communicate, one or more external communications inresponse to input data indicating an express request for acommunication. In another example, the semantic interface unit 3600 maygenerate and transmit, send, or otherwise communicate, one or moreexternal communications in response to one or more defined events, suchas the expiration of a recency of access period for a user.

Although shown as a single unit in FIG. 3, the relational search unit3700 may be implemented in a distributed configuration, which mayinclude a primary relational search unit instance and one or moresecondary relational search unit instances.

The relational search unit 3700 may generate, maintain, operate, or acombination thereof, one or more indexes, such as one or more of anontological index, a constituent data index, a control-word index, anumeral index, or a constant index, based on the low-latency data storedin the distributed in-memory database 3300, the low-latency databaseanalysis system 3000, or both. An index may be a defined data structure,or combination of data structures, for storing tokens, terms, or stringkeys, representing a set of data from one or more defined data sourcesin a form optimized for searching. For example, an index may be acollection of index shards. In some implementations, an index may besegmented into index segments and the index segments may be sharded intoindex shards. In some implementations, an index may be partitioned intoindex partitions, the index partitions may be segmented into indexsegments and the index segments may be sharded into index shards.

Generating, or building, an index may be performed to create or populatea previously unavailable index, which may be referred to as indexing thecorresponding data, and may include regenerating, rebuilding, orreindexing to update or modify a previously available index, such as inresponse to a change in the indexed data (constituent data).

The ontological index may be an index of data (ontological data)describing the ontological structure or schema of the low-latencydatabase analysis system 3000, the low-latency data stored in thedistributed in-memory database 3300, or a combination thereof. Forexample, the ontological index may include data representing the tableand column structure of the distributed in-memory database 3300. Therelational search unit 3700 may generate, maintain, or both, theontological index by communicating with, such as requesting ontologicaldata from, the distributed in-memory ontology unit 3500, the semanticinterface unit 3600, or both. Each record in the ontological index maycorrespond to a respective ontological token, such as a token thatidentifies a column by name.

The control-word index may be an index of a defined set of control-wordtokens. A control-word token may be a character, a symbol, a word, or adefined ordered sequence of characters or symbols, that is identified inone or more grammars of the low-latency database analysis system 3000 ashaving one or more defined grammatical functions, which may becontextual. For example, the control-word index may include thecontrol-word token “sum”, which may be identified in one or moregrammars of the low-latency database analysis system 3000 as indicatingan additive aggregation. In another example, the control-word index mayinclude the control-word token “top”, which may be identified in one ormore grammars of the low-latency database analysis system 3000 asindicating a maximal value from an ordered set. In another example, thecontrol-word index may include operator tokens, such as the equalityoperator token (“=”). The constant index may be an index of constanttokens such as “100” or “true”. The numeral index may be an index ofnumber word tokens (or named numbers), such as number word tokens forthe positive integers between zero and one million, inclusive. Forexample, “one hundred and twenty eight”.

A token may be a word, phrase, character, sequence of characters,symbol, combination of symbols, or the like. A token may represent adata portion in the low-latency data stored in the low-latency datastructure. For example, the relational search unit 3700 mayautomatically generate respective tokens representing the attributes,the measures, the tables, the columns, the values, unique identifiers,tags, links, keys, or any other data portion, or combination of dataportions, or a portion thereof. The relational search unit 3700 mayclassify the tokens, which may include storing token classification datain association with the tokens. For example, a token may be classifiedas an attribute token, a measure token, a value token, or the like.

The constituent data index may be an index of the constituent datavalues stored in the low-latency database analysis system 3000, such asin the distributed in-memory database 3300. The relational search unit3700 may generate, maintain, or both, the constituent data index bycommunicating with, such as requesting data from, the distributedin-memory database 3300. For example, the relational search unit 3700may send, or otherwise communicate, a message or signal to thedistributed in-memory database 3300 indicating a request to perform anindexing data-query, the relational search unit 3700 may receiveresponse data from the distributed in-memory database 3300 in responseto the requested indexing data-query, and the relational search unit3700 may generate the constituent data index, or a portion thereof,based on the response data. For example, the constituent data index mayindex data-objects.

An index shard may be used for token searching, such as exact matchsearching, prefix match searching, substring match searching, or suffixmatch searching. Exact match searching may include identifying tokens inthe index shard that matches a defined target value. Prefix matchsearching may include identifying tokens in the index shard that includea prefix, or begin with a value, such as a character or string, thatmatches a defined target value. Substring match searching may includeidentifying tokens in the index shard that include a value, such as acharacter or string, that matches a defined target value. Suffix matchsearching may include identifying tokens in the index shard that includea suffix, or end with a value, such as a character or string, thatmatches a defined target value. In some implementations, an index shardmay include multiple distinct index data structures. For example, anindex shard may include a first index data structure optimized for exactmatch searching, prefix match searching, and suffix match searching, anda second index data structure optimized for sub string match searching.Traversing, or otherwise accessing, managing, or using, an index mayinclude identifying one or more of the index shards of the index andtraversing the respective index shards. In some implementations, one ormore indexes, or index shards, may be distributed, such as replicated onmultiple relational search unit instances. For example, the ontologicalindex may be replicated on each relational search unit instance.

The relational search unit 3700 may receive a request for data from thelow-latency database analysis system 3000. For example, the relationalsearch unit 3700 may receive data expressing a usage intent indicatingthe request for data in response to input, such as user input, obtainedvia a user interface, such as a user interface generated, or partiallygenerated, by the system access interface unit 3900, which may be a userinterface operated on an external device, such as one of the clientdevices 2320, 2340 shown in FIG. 2. In some implementations, therelational search unit 3700 may receive the data expressing the usageintent from the system access interface unit 3900 or from the semanticinterface unit 3600. For example, the relational search unit 3700 mayreceive or access the data expressing the usage intent in a request fordata message or signal.

The relational search unit 3700 may process, parse, identify semantics,tokenize, or a combination thereof, the request for data to generate aresolved-request, which may include identifying a database andvisualization agnostic ordered sequence of tokens based on the dataexpressing the usage intent. The data expressing the usage intent, orrequest for data, may include request data, such as resolved requestdata, unresolved request data, or a combination of resolved request dataand unresolved request data. The relational search unit 3700 mayidentify the resolved request data. The relational search unit 3700 mayidentify the unresolved request data and may tokenize the unresolvedrequest data.

Resolved request data may be request data identified in the dataexpressing the usage intent as resolved request data. Each resolvedrequest data portion may correspond with a respective token in thelow-latency database analysis system 3000. The data expressing the usageintent may include information identifying one or more portions of therequest data as resolved request data.

Unresolved request data may be request data identified in the dataexpressing the usage intent as unresolved request data, or request datafor which the data expressing the usage intent omits informationidentifying the request data a resolved request data. Unresolved requestdata may include text or string data, which may include a character,sequence of characters, symbol, combination of symbols, word, sequenceof words, phrase, or the like, for which information, such astokenization binding data, identifying the text or string data asresolved request data is absent or omitted from the request data. Thedata expressing the usage intent may include information identifying oneor more portions of the request data as unresolved request data. Thedata expressing the usage intent may omit information identifyingwhether one or more portions of the request data are resolved requestdata. The relational search unit 3700 may identify one or more portionsof the request data for which the data expressing the usage intent omitsinformation identifying whether the one or more portions of the requestdata are resolved request data as unresolved request data.

For example, the data expressing the usage intent may include a requeststring and one or more indications that one or more portions of therequest string are resolved request data. One or more portions of therequest string that are not identified as resolved request data in thedata expressing the usage intent may be identified as unresolved requestdata. For example, the data expressing the usage intent may include therequest string “example text”; the data expressing the usage intent mayinclude information indicating that the first portion of the requeststring, “example”, is resolved request data; and the data expressing theusage intent may omit information indicating that the second portion ofthe request string, “text”, is resolved request data.

The information identifying one or more portions of the request data asresolved request data may include tokenization binding data indicating apreviously identified token corresponding to the respective portion ofthe request data. The tokenization binding data corresponding to arespective token may include, for example, one or more of a columnidentifier indicating a column corresponding to the respective token, adata type identifier corresponding to the respective token, a tableidentifier indicating a table corresponding to the respective token, anindication of an aggregation corresponding to the respective token, oran indication of a join path associated with the respective token. Othertokenization binding data may be used. In some implementations, the dataexpressing the usage intent may omit the tokenization binding data andmay include an identifier that identifies the tokenization binding data.

The relational search unit 3700 may implement or access one or moregrammar-specific tokenizers, such as a tokenizer for a definedrelational search grammar or a tokenizer for a natural-language grammar.For example, the relational search unit 3700 may implement one or moreof a formula tokenizer, a row-level-security tokenizer, a relationalsearch tokenizer, or a natural language tokenizer. Other tokenizers maybe used. In some implementations, the relational search unit 3700 mayimplement one or more of the grammar-specific tokenizers, or a portionthereof, by accessing another component of the low-latency databaseanalysis system 3000 that implements the respective grammar-specifictokenizer, or a portion thereof. For example, the natural languageprocessing unit 3710 may implement the natural language tokenizer andthe relational search unit 3700 may access the natural languageprocessing unit 3710 to implement natural language tokenization.

A tokenizer, such as the relational search tokenizer, may parse text orstring data (request string), such as string data included in a dataexpressing the usage intent, in a defined read order, such as from leftto right, such as on a character-by-character or symbol-by-symbol basis.For example, a request string may include a single character, symbol, orletter, and tokenization may include identifying one or more tokensmatching, or partially matching, the input character.

Tokenization may include parsing the request string to identify one ormore words or phrases. For example, the request string may include asequence of characters, symbols, or letters, and tokenization mayinclude parsing the sequence of characters in a defined order, such asfrom left to right, to identify distinct words or terms and identifyingone or more tokens matching the respective words. In someimplementations, word or phrase parsing may be based on one or more of aset of defined delimiters, such as a whitespace character, a punctuationcharacter, or a mathematical operator.

The relational search unit 3700 may traverse one or more of the indexesto identify one or more tokens corresponding to a character, word, orphrase identified in request string. Tokenization may includeidentifying multiple candidate tokens matching a character, word, orphrase identified in request string. Candidate tokens may be ranked orordered, such as based on probabilistic utility.

Tokenization may include match-length maximization. Match-lengthmaximization may include ranking or ordering candidate matching tokensin descending magnitude order. For example, the longest candidate token,having the largest cardinality of characters or symbols, matching therequest string, or a portion thereof, may be the highest rankedcandidate token. For example, the request string may include a sequenceof words or a semantic phrase, and tokenization may include identifyingone or more tokens matching the input semantic phrase. In anotherexample, the request string may include a sequence of phrases, andtokenization may include identifying one or more tokens matching theinput word sequence. In some implementations, tokenization may includeidentifying the highest ranked candidate token for a portion of therequest string as a resolved token for the portion of the requeststring.

The relational search unit 3700 may implement one or more finite statemachines. For example, tokenization may include using one or more finitestate machines. A finite state machine may model or represent a definedset of states and a defined set of transitions between the states. Astate may represent a condition of the system represented by the finitestate machine at a defined temporal point. A finite state machine maytransition from a state (current state) to a subsequent state inresponse to input (e.g., input to the finite state machine). Atransition may define one or more actions or operations that therelational search unit 3700 may implement. One or more of the finitestate machines may be non-deterministic, such that the finite statemachine may transition from a state to zero or more subsequent states.

The relational search unit 3700 may generate, instantiate, or operate atokenization finite state machine, which may represent the respectivetokenization grammar. Generating, instantiating, or operating a finitestate machine may include operating a finite state machine traverser fortraversing the finite state machine. Instantiating the tokenizationfinite state machine may include entering an empty state, indicating theabsence of received input. The relational search unit 3700 may initiateor execute an operation, such as an entry operation, corresponding tothe empty state in response to entering the empty state. Subsequently,the relational search unit 3700 may receive input data, and thetokenization finite state machine may transition from the empty state toa state corresponding to the received input data. In some embodiments,the relational search unit 3700 may initiate one or more data-queries inresponse to transitioning to or from a respective state of a finitestate machine. In the tokenization finite state machine, a state mayrepresent a possible next token in the request string. The tokenizationfinite state machine may transition between states based on one or moredefined transition weights, which may indicate a probability oftransiting from a state to a subsequent state.

The tokenization finite state machine may determine tokenization basedon probabilistic path utility. Probabilistic path utility may rank ororder multiple candidate traversal paths for traversing the tokenizationfinite state machine based on the request string. The candidate pathsmay be ranked or ordered based on one or more defined probabilistic pathutility metrics, which may be evaluated in a defined sequence. Forexample, the tokenization finite state machine may determineprobabilistic path utility by evaluating the weights of the respectivecandidate transition paths, the lengths of the respective candidatetransition paths, or a combination thereof. In some implementations, theweights of the respective candidate transition paths may be evaluatedwith high priority relative to the lengths of the respective candidatetransition paths.

In some implementations, one or more transition paths evaluated by thetokenization finite state machine may include a bound state such thatthe candidate tokens available for tokenization of a portion of therequest string may be limited based on the tokenization of a previouslytokenized portion of the request string.

Tokenization may include matching a portion of the request string to oneor more token types, such as a constant token type, a column name tokentype, a value token type, a control-word token type, a date value tokentype, a string value token type, or any other token type defined by thelow-latency database analysis system 3000. A constant token type may bea fixed, or invariant, token type, such as a numeric value. A columnname token type may correspond with a name of a column in the datamodel. A value token type may correspond with an indexed data value. Acontrol-word token type may correspond with a defined set ofcontrol-words. A date value token type may be similar to a control-wordtoken type and may correspond with a defined set of control-words fordescribing temporal information. A string value token type maycorrespond with an unindexed value.

Token matching may include ordering or weighting candidate token matchesbased on one or more token matching metrics. Token matching metrics mayinclude whether a candidate match is within a defined data scope, suchas a defined set of tables, wherein a candidate match outside thedefined data scope (out-of-scope) may be ordered or weighted lower thana candidate match within the define data scope (in-scope). Tokenmatching metrics may include whether, or the degree to which, acandidate match increases query complexity, such as by spanning multipleroots, wherein a candidate match that increases complexity may beordered or weighted lower than a candidate match that does not increasecomplexity or increases complexity to a lesser extent. Token matchingmetrics may include whether the candidate match is an exact match or apartial match, wherein a candidate match that is a partial may beordered or weighted lower than a candidate match that is an exact match.In some implementations, the cardinality of the set of partial matchesmay be limited to a defined value.

Token matching metrics may include a token score (TokenScore), wherein acandidate match with a relatively low token score may be ordered orweighted lower than a candidate match with a relatively high tokenscore. The token score for a candidate match may be determined based oneor more token scoring metrics. The token scoring metrics may include afinite state machine transition weight metric (FSMScore), wherein aweight of transitioning from a current state of the tokenization finitestate machine to a state indicating a candidate matching token is thefinite state machine transition weight metric. The token scoring metricsmay include a cardinality penalty metric (CardinalityScore), wherein acardinality of values (e.g., unique values) corresponding to thecandidate matching token is used as a penalty metric (inversecardinality), which may reduce the token score. The token scoringmetrics may include an index utility metric (IndexScore), wherein adefined utility value, such as one, associated with an object, such as acolumn wherein the matching token represents the column or a value fromthe column, is the index utility metric. In some implementations, thedefined utility values may be configured, such as in response to userinput, on a per object (e.g., per column) basis. The token scoringmetrics may include a usage metric (UBRScore). The usage metric may bedetermined based on a usage based ranking index, one or more usageranking metrics, or a combination thereof. Determining the usage metric(UBRScore) may include determining a usage boost value (UBRBoost). Thetoken score may be determined based on a defined combination of tokenscoring metrics. For example, determining the token score may beexpressed as the following:

TokenScore=FSMScore*(IndexScore+UBRScore*UBRBoost)+Min(CardinalityScore, 1).

Token matching may include grouping candidate token matches by matchtype, ranking or ordering on a per-match type basis based on tokenscore, and ranking or ordering the match types. For example, the matchtypes may include a first match type for exact matches (having thehighest match type priority order), a second match type for prefixmatches on ontological data (having a match type priority order lowerthan the first match type), a third match type for substring matches onontological data and prefix matches on data values (having a match typepriority order lower than the second match type), a fourth match typefor substring matches on data values (having a match type priority orderlower than the third match type), and a fifth match type for matchesomitted from the first through fourth match types (having a match typepriority order lower than the fourth match type). Other match types andmatch type orders may be used.

Tokenization may include ambiguity resolution. Ambiguity resolution mayinclude token ambiguity resolution, join-path ambiguity resolution, orboth. In some implementations, ambiguity resolution may ceasetokenization in response to the identification of an automatic ambiguityresolution error or failure.

Token ambiguity may correspond with identifying two or more exactlymatching candidate matching tokens. Token ambiguity resolution may bebased on one or more token ambiguity resolution metrics. The tokenambiguity resolution metrics may include using available previouslyresolved token matching or binding data and token ambiguity may beresolved in favor of available previously resolved token matching orbinding data, other relevant tokens resolved from the request string, orboth. The token ambiguity resolution may include resolving tokenambiguity in favor of integer constants. The token ambiguity resolutionmay include resolving token ambiguity in favor of control-words, such asfor tokens at the end of a request for data, such as last, that are notbeing edited.

Join-path ambiguity may correspond with identifying matching tokenshaving two or more candidate join paths. Join-path ambiguity resolutionmay be based on one or more join-path ambiguity resolution metrics. Thejoin-path ambiguity resolution metrics may include using availablepreviously resolved join-path binding data and join-path ambiguity maybe resolved in favor of available previously resolved join-paths. Thejoin-path ambiguity resolution may include favoring join paths thatinclude in-scope objects over join paths that include out-of-scopeobjects. The join-path ambiguity resolution metrics may include acomplexity minimization metric, which may favor a join path that omitsor avoids increasing complexity over join paths that increasecomplexity, such as a join path that may introduce a chasm trap.

The relational search unit 3700 may identify a resolved-request based onthe request string. The resolved-request, which may be database andvisualization agnostic, may be expressed or communicated as an orderedsequence of tokens representing the request for data indicated by therequest string. The relational search unit 3700 may instantiate, orgenerate, one or more resolved-request objects. For example, therelational search unit 3700 may create or store a resolved-requestobject corresponding to the resolved-request in the distributedin-memory ontology unit 3500. The relational search unit 3700 maytransmit, send, or otherwise make available, the resolved-request to thesemantic interface unit 3600.

In some implementations, the relational search unit 3700 may transmit,send, or otherwise make available, one or more resolved-requests, orportions thereof, to the semantic interface unit 3600 in response tofinite state machine transitions. For example, the relational searchunit 3700 may instantiate a search object in response to a firsttransition of a finite state machine. The relational search unit 3700may include a first search object instruction in the search object inresponse to a second transition of the finite state machine. Therelational search unit 3700 may send the search object including thefirst search object instruction to the semantic interface unit 3600 inresponse to the second transition of the finite state machine. Therelational search unit 3700 may include a second search objectinstruction in the search object in response to a third transition ofthe finite state machine. The relational search unit 3700 may send thesearch object including the search object instruction, or a combinationof the first search object instruction and the second search objectinstruction, to the semantic interface unit 3600 in response to thethird transition of the finite state machine. The search objectinstructions may be represented using any annotation, instruction, text,message, list, pseudo-code, comment, or the like, or any combinationthereof that may be converted, transcoded, or translated into structuredsearch instructions for retrieving data from the low-latency data.

The relational search unit 3700 may provide an interface to permit thecreation of user-defined syntax. For example, a user may associate astring with one or more tokens. Accordingly, when the string is entered,the pre-associated tokens are returned in lieu of searching for tokensto match the input.

The relational search unit 3700 may include a localization unit (notexpressly shown). The localization, globalization, regionalization, orinternationalization, unit may obtain source data expressed inaccordance with a source expressive-form and may output destination datarepresenting the source data, or a portion thereof, and expressed usinga destination expressive-form. The data expressive-forms, such as thesource expressive-form and the destination expressive-form, may includeregional or customary forms of expression, such as numeric expression,temporal expression, currency expression, alphabets, natural-languageelements, measurements, or the like. For example, the sourceexpressive-form may be expressed using a canonical-form, which mayinclude using a natural-language, which may be based on English, and thedestination expressive-form may be expressed using a locale-specificform, which may include using another natural-language, which may be anatural-language that differs from the canonical-language. In anotherexample, the destination expressive-form and the source expressive-formmay be locale-specific expressive-forms and outputting the destinationexpressive-form representation of the source expressive-form data mayinclude obtaining a canonical-form representation of the sourceexpressive-form data and obtaining the destination expressive-formrepresentation based on the canonical-form representation. Although, forsimplicity and clarity, the grammars described herein, such as therelational search grammar and the natural language search grammar, aredescribed with relation to the canonical expressive-form, theimplementation of the respective grammars, or portions thereof,described herein may implement locale-specific expressive-forms. Forexample, the relational search tokenizer may include multiplelocale-specific relational search tokenizers.

The natural language processing unit 3710 may receive input dataincluding a natural language string, such as a natural language stringgenerated in accordance with user input. The natural language string mayrepresent a data request expressed in an unrestricted natural languageform, for which data identified or obtained prior to, or in conjunctionwith, receiving the natural language string by the natural languageprocessing unit 3710 indicating the semantic structure, correlation tothe low-latency database analysis system 3000, or both, for at least aportion of the natural language string is unavailable or incomplete.Although not shown separately in FIG. 3, in some implementations, thenatural language string may be generated or determined based onprocessing an analog signal, or a digital representation thereof, suchas an audio stream or recording or a video stream or recording, whichmay include using speech-to-text conversion.

The natural language processing unit 3710 may analyze, process, orevaluate the natural language string, or a portion thereof, to generateor determine the semantic structure, correlation to the low-latencydatabase analysis system 3000, or both, for at least a portion of thenatural language string. For example, the natural language processingunit 3710 may identify one or more words or terms in the naturallanguage string and may correlate the identified words to tokens definedin the low-latency database analysis system 3000. In another example,the natural language processing unit 3710 may identify a semanticstructure for the natural language string, or a portion thereof. Inanother example, the natural language processing unit 3710 may identifya probabilistic intent for the natural language string, or a portionthereof, which may correspond to an operative feature of the low-latencydatabase analysis system 3000, such as retrieving data from the internaldata, analyzing data the internal data, or modifying the internal data.

The natural language processing unit 3710 may send, transmit, orotherwise communicate request data indicating the tokens, relationships,semantic data, probabilistic intent, or a combination thereof or one ormore portions thereof, identified based on a natural language string tothe relational search unit 3700.

The data utility unit 3720 may receive, process, and maintainuser-agnostic utility data, such as system configuration data,user-specific utility data, such as utilization data, or bothuser-agnostic and user-specific utility data. The utility data mayindicate whether a data portion, such as a column, a record, an insight,or any other data portion, has high utility or low utility within thesystem, such across all users of the system. For example, the utilitydata may indicate that a defined column is a high-utility column or alow-utility column. The data utility unit 3720 may store the utilitydata, such as using the low-latency data structure. For example, inresponse to a user using, or accessing, a data portion, data utilityunit 3720 may store utility data indicating the usage, or access, eventfor the data portion, which may include incrementing a usage eventcounter associated with the data portion. In some embodiments, the datautility unit 3720 may receive the information indicating the usage, oraccess, event for the data portion from the insight unit 3730, and theusage, or access, event for the data portion may indicate that the usageis associated with an insight.

The data utility unit 3720 may receive a signal, message, or othercommunication, indicating a request for utility information. The requestfor utility information may indicate an object or data portion. The datautility unit 3720 may determine, identify, or obtain utility dataassociated with the identified object or data portion. The data utilityunit 3720 may generate and send utility response data responsive to therequest that may indicate the utility data associated with theidentified object or data portion.

The data utility unit 3720 may generate, maintain, operate, or acombination thereof, one or more indexes, such as one or more of a usage(or utility) index, a resolved-request index, or a phrase index, basedon the low-latency data stored in the distributed in-memory database3300, the low-latency database analysis system 3000, or both.

The insight unit 3730 may automatically identify one or more insights,which may be data other than data expressly requested by a user, andwhich may be identified and prioritized, or both, based on probabilisticutility.

The object search unit 3800 may generate, maintain, operate, or acombination thereof, one or more object-indexes, which may be based onthe analytical-objects represented in the low-latency database analysissystem 3000, or a portion thereof, such as pinboards, answers, andworksheets. An object-index may be a defined data structure, orcombination of data structures, for storing analytical-object data in aform optimized for searching. Although shown as a single unit in FIG. 3,the object search unit 3800 may interface with a distinct, separate,object indexing unit (not expressly shown).

The object search unit 3800 may include an object-index populationinterface, an object-index search interface, or both. The object-indexpopulation interface may obtain and store, load, or populateanalytical-object data, or a portion thereof, in the object-indexes. Theobject-index search interface may efficiently access or retrieveanalytical-object data from the object-indexes such as by searching ortraversing the object-indexes, or one or more portions thereof. In someimplementations, the object-index population interface, or a portionthereof, may be a distinct, independent unit.

The object-index population interface may populate, update, or both theobject-indexes, such as periodically, such as in accordance with adefined temporal period, such as thirty minutes. Populating, orupdating, the object-indexes may include obtaining object indexing datafor indexing the analytical-objects represented in the low-latencydatabase analysis system 3000. For example, the object-index populationinterface may obtain the analytical-object indexing data, such as fromthe distributed in-memory ontology unit 3500. Populating, or updating,the object-indexes may include generating or creating an indexing datastructure representing an object. The indexing data structure forrepresenting an object may differ from the data structure used forrepresenting the object in other components of the low-latency databaseanalysis system 3000, such as in the distributed in-memory ontology unit3500.

The object indexing data for an analytical-object may be a subset of theobject data for the analytical-object. The object indexing data for ananalytical-object may include an object identifier for theanalytical-object uniquely identifying the analytical-object in thelow-latency database analysis system 3000, or in a defined data-domainwithin the low-latency database analysis system 3000. The low-latencydatabase analysis system 3000 may uniquely, unambiguously, distinguishan object from other objects based on the object identifier associatedwith the object. The object indexing data for an analytical-object mayinclude data non-uniquely identifying the object. The low-latencydatabase analysis system 3000 may identify one or moreanalytical-objects based on the non-uniquely identifying data associatedwith the respective objects, or one or more portions thereof. In someimplementations, an object identifier may be an ordered combination ofnon-uniquely identifying object data that, as expressed in the orderedcombination, is uniquely identifying. The low-latency database analysissystem 3000 may enforce the uniqueness of the object identifiers.

Populating, or updating, the object-indexes may include indexing theanalytical-object by including or storing the object indexing data inthe object-indexes. For example, the object indexing data may includedata for an analytical-object, the object-indexes may omit data for theanalytical-object, and the object-index population interface may includeor store the object indexing data in an object-index. In anotherexample, the object indexing data may include data for ananalytical-object, the object-indexes may include data for theanalytical-object, and the object-index population interface may updatethe object indexing data for the analytical-object in the object-indexesin accordance with the object indexing data.

Populating, or updating, the object-indexes may include obtaining objectutility data for the analytical-objects represented in the low-latencydatabase analysis system 3000. For example, the object-index populationinterface may obtain the object utility data, such as from the objectutility unit 3810. The object-index population interface may include theobject utility data in the object-indexes in association with thecorresponding objects.

In some implementations, the object-index population interface mayreceive, obtain, or otherwise access the object utility data from adistinct, independent, object utility data population unit, which mayread, obtain, or otherwise access object utility data from the objectutility unit 3810 and may send, transmit, or otherwise provide, theobject utility data to the object search unit 3800. The object utilitydata population unit may send, transmit, or otherwise provide, theobject utility data to the object search unit 3800 periodically, such asin accordance with a defined temporal period, such as thirty minutes.

The object-index search interface may receive, access, or otherwiseobtain data expressing a usage intent with respect to the low-latencydatabase analysis system 3000, which may represent a request to accessdata in the low-latency database analysis system 3000, which mayrepresent a request to access one or more analytical-objects representedin the low-latency database analysis system 3000. The object-indexsearch interface may generate one or more object-index queries based onthe data expressing the usage intent. The object-index search interfacemay send, transmit, or otherwise make available the object-index queriesto one or more of the object-indexes.

The object-index search interface may receive, obtain, or otherwiseaccess object search results data indicating one or moreanalytical-objects identified by searching or traversing theobject-indexes in accordance with the object-index queries. Theobject-index search interface may sort or rank the object search resultsdata based on probabilistic utility in accordance with the objectutility data for the analytical-objects in the object search resultsdata. In some implementations, the object-index search interface mayinclude one or more object search ranking metrics with the object-indexqueries and may receive the object search results data sorted or rankedbased on probabilistic utility in accordance with the object utilitydata for the objects in the object search results data and in accordancewith the object search ranking metrics.

For example, the data expressing the usage intent may include a useridentifier, and the object search results data may include object searchresults data sorted or ranked based on probabilistic utility for theuser. In another example, the data expressing the usage intent mayinclude a user identifier and one or more search terms, and the objectsearch results data may include object search results data sorted orranked based on probabilistic utility for the user identified bysearching or traversing the object-indexes in accordance with the searchterms.

The object-index search interface may generate and send, transmit, orotherwise make available the sorted or ranked object search results datato another component of the low-latency database analysis system 3000,such as for further processing and display to the user.

The object utility unit 3810 may receive, process, and maintainuser-specific object utility data for objects represented in thelow-latency database analysis system 3000. The user-specific objectutility data may indicate whether an object has high utility or lowutility for the user.

The object utility unit 3810 may store the user-specific object utilitydata, such as on a per-object basis, a per-activity basis, or both. Forexample, in response to data indicating an object access activity, suchas a user using, viewing, or otherwise accessing, an object, the objectutility unit 3810 may store user-specific object utility data indicatingthe object access activity for the object, which may includeincrementing an object access activity counter associated with theobject, which may be a user-specific object access activity counter. Inanother example, in response to data indicating an object storageactivity, such as a user storing an object, the object utility unit 3810may store user-specific object utility data indicating the objectstorage activity for the object, which may include incrementing astorage activity counter associated with the object, which may be auser-specific object storage activity counter. The user-specific objectutility data may include temporal information, such as a temporallocation identifier associated with the object activity. Otherinformation associated with the object activity may be included in theobject utility data.

The object utility unit 3810 may receive a signal, message, or othercommunication, indicating a request for object utility information. Therequest for object utility information may indicate one or more objects,one or more users, one or more activities, temporal information, or acombination thereof. The request for object utility information mayindicate a request for object utility data, object utility counter data,or both.

The object utility unit 3810 may determine, identify, or obtain objectutility data in accordance with the request for object utilityinformation. The object utility unit 3810 may generate and send objectutility response data responsive to the request that may indicate theobject utility data, or a portion thereof, in accordance with therequest for object utility information.

For example, a request for object utility information may indicate auser, an object, temporal information, such as information indicating atemporal span, and an object activity, such as the object accessactivity. The request for object utility information may indicate arequest for object utility counter data. The object utility unit 3810may determine, identify, or obtain object utility counter dataassociated with the user, the object, and the object activity having atemporal location within the temporal span, and the object utility unit3810 may generate and send object utility response data including theidentified object utility counter data.

In some implementations, a request for object utility information mayindicate multiple users, or may omit indicating a user, and the objectutility unit 3810 may identify user-agnostic object utility dataaggregating the user-specific object utility data. In someimplementations, a request for object utility information may indicatemultiple objects, may omit indicating an object, or may indicate anobject type, such as answer, pinboard, or worksheet, and the objectutility unit 3810 may identify the object utility data by aggregatingthe object utility data for multiple objects in accordance with therequest. Other object utility aggregations may be used.

The system configuration unit 3820 implement or apply one or morelow-latency database analysis system configurations to enable, disable,or configure one or more operative features of the low-latency databaseanalysis system 3000. The system configuration unit 3820 may store datarepresenting or defining the one or more low-latency database analysissystem configurations. The system configuration unit 3820 may receivesignals or messages indicating input data, such as input data generatedvia a system access interface, such as a user interface, for accessingor modifying the low-latency database analysis system configurations.The system configuration unit 3820 may generate, modify, delete, orotherwise maintain the low-latency database analysis systemconfigurations, such as in response to the input data. The systemconfiguration unit 3820 may generate or determine output data, and mayoutput the output data, for a system access interface, or a portion orportions thereof, for the low-latency database analysis systemconfigurations, such as for presenting a user interface for thelow-latency database analysis system configurations. Although not shownin FIG. 3, the system configuration unit 3820 may communicate with arepository, such as an external centralized repository, of low-latencydatabase analysis system configurations; the system configuration unit3820 may receive one or more low-latency database analysis systemconfigurations from the repository, and may control or configure one ormore operative features of the low-latency database analysis system 3000in response to receiving one or more low-latency database analysissystem configurations from the repository.

The user customization unit 3830 may receive, process, and maintainuser-specific utility data, such as user defined configuration data,user defined preference data, or a combination thereof. Theuser-specific utility data may indicate whether a data portion, such asa column, a record, an insight, or any other data portion or object, hashigh utility or low utility to an identified user. For example, theuser-specific utility data may indicate that a defined column is ahigh-utility column or a low-utility column. The user customization unit3830 may store the user-specific utility data, such as using thelow-latency data structure. The user customization unit 3830 may storethe feedback at an individual level and may include the context in whichfeedback was received from the user. Feedback may be stored in adisk-based system. In some implementations, feedback may be stored in anin-memory storage.

The system access interface unit 3900 may interface with, or communicatewith, a system access unit (not shown in FIG. 3), which may be a clientdevice, a user device, or another external device or system, or acombination thereof, to provide access to the internal data, features ofthe low-latency database analysis system 3000, or a combination thereof.For example, the system access interface unit 3900 may receive signals,message, or other communications representing interactions with theinternal data, such as data expressing a usage intent and may outputresponse messages, signals, or other communications responsive to thereceived requests.

The system access interface unit 3900 may generate data for presenting auser interface, or one or more portions thereof, for the low-latencydatabase analysis system 3000. For example, the system access interfaceunit 3900 may generate instructions for rendering, or otherwisepresenting, the user interface, or one or more portions thereof and maytransmit, or otherwise make available, the instructions for rendering,or otherwise presenting, the user interface, or one or more portionsthereof to the system access unit, for presentation to a user of thesystem access unit. For example, the system access unit may present theuser interface via a web browser or a web application and theinstructions may be in the form of HTML, JavaScript, or the like.

In an example, the system access interface unit 3900 may include asearch field user interface element in the user interface. The searchfield user interface element may be an unstructured search string userinput element or field. The system access unit may display theunstructured search string user input element. The system access unitmay receive input data, such as user input data, corresponding to theunstructured search string user input element. The system access unitmay transmit, or otherwise make available, the unstructured searchstring user input to the system access interface unit 3900. The userinterface may include other user interface elements and the systemaccess unit may transmit, or otherwise make available, other user inputdata to the system access interface unit 3900.

The system access interface unit 3900 may obtain the user input data,such as the unstructured search string, from the system access unit. Thesystem access interface unit 3900 may transmit, or otherwise makeavailable, the user input data to one or more of the other components ofthe low-latency database analysis system 3000.

In some embodiments, the system access interface unit 3900 may obtainthe unstructured search string user input as a sequence of individualcharacters or symbols, and the system access interface unit 3900 maysequentially transmit, or otherwise make available, individual or groupsof characters or symbols of the user input data to one or more of theother components of the low-latency database analysis system 3000.

In some embodiments, system access interface unit 3900 may obtain theunstructured search string user input may as a sequence of individualcharacters or symbols, the system access interface unit 3900 mayaggregate the sequence of individual characters or symbols, and maysequentially transmit, or otherwise make available, a currentaggregation of the received user input data to one or more of the othercomponents of the low-latency database analysis system 3000, in responseto receiving respective characters or symbols from the sequence, such ason a per-character or per-symbol basis.

The real-time collaboration unit 3910 may receive signals or messagesrepresenting input received in accordance with multiple users, ormultiple system access devices, associated with a collaboration contextor session, may output data, such as visualizations, generated ordetermined by the low-latency database analysis system 3000 to multipleusers associated with the collaboration context or session, or both. Thereal-time collaboration unit 3910 may receive signals or messagesrepresenting input received in accordance with one or more usersindicating a request to establish a collaboration context or session,and may generate, maintain, or modify collaboration data representingthe collaboration context or session, such as a collaboration sessionidentifier. The real-time collaboration unit 3910 may receive signals ormessages representing input received in accordance with one or moreusers indicating a request to participate in, or otherwise associatewith, a currently active collaboration context or session, and mayassociate the one or more users with the currently active collaborationcontext or session. In some implementations, the input, output, or both,of the real-time collaboration unit 3910 may include synchronizationdata, such as temporal data, that may be used to maintainsynchronization, with respect to the collaboration context or session,among the low-latency database analysis system 3000 and one or moresystem access devices associated with, or otherwise accessing, thecollaboration context or session.

The third-party integration unit 3920 may include an electroniccommunication interface, such as an application programming interface(API), for interfacing or communicating between an external, such asthird-party, application or system, and the low-latency databaseanalysis system 3000. For example, the third-party integration unit 3920may include an electronic communication interface to transfer databetween the low-latency database analysis system 3000 and one or moreexternal applications or systems, such as by importing data into thelow-latency database analysis system 3000 from the external applicationsor systems or exporting data from the low-latency database analysissystem 3000 to the external applications or systems. For example, thethird-party integration unit 3920 may include an electroniccommunication interface for electronic communication with an externalexchange, transfer, load (ETL) system, which may import data into thelow-latency database analysis system 3000 from an external data sourceor may export data from the low-latency database analysis system 3000 toan external data repository. In another example, the third-partyintegration unit 3920 may include an electronic communication interfacefor electronic communication with external machine learning analysissoftware, which may export data from the low-latency database analysissystem 3000 to the external machine learning analysis software and mayimport data into the low-latency database analysis system 3000 from theexternal machine learning analysis software. The third-party integrationunit 3920 may transfer data independent of, or in conjunction with, thesystem access interface unit 3900, the enterprise data interface unit3400, or both.

The persistent storage unit 3930 may include an interface for storingdata on, accessing data from, or both, one or more persistent datastorage devices or systems. For example, the persistent storage unit3930 may include one or more persistent data storage devices, such asthe static memory 1200 shown in FIG. 1. Although shown as a single unitin FIG. 3, the persistent storage unit 3930 may include multiplecomponents, such as in a distributed or clustered configuration. Thepersistent storage unit 3930 may include one or more internalinterfaces, such as electronic communication or application programminginterfaces, for receiving data from, sending data to, or both othercomponents of the low-latency database analysis system 3000. Thepersistent storage unit 3930 may include one or more externalinterfaces, such as electronic communication or application programminginterfaces, for receiving data from, sending data to, or both, one ormore external systems or devices, such as an external persistent storagesystem. For example, the persistent storage unit 3930 may include aninternal interface for obtaining key-value tuple data from othercomponents of the low-latency database analysis system 3000, an externalinterface for sending the key-value tuple data to, or storing thekey-value tuple data on, an external persistent storage system, anexternal interface for obtaining, or otherwise accessing, the key-valuetuple data from the external persistent storage system, and an internalkey-value tuple data for sending, or otherwise making available, thekey-value tuple data to other components of the low-latency databaseanalysis system 3000. In another example, the persistent storage unit3930 may include a first external interface for storing data on, orobtaining data from, a first external persistent storage system, and asecond external interface for storing data on, or obtaining data from, asecond external persistent storage system.

FIG. 4 is a block diagram of an example of conversational databaseanalysis in accordance with this disclosure. Conversational databaseanalysis may be implemented using a low-latency database analysis system4000 which includes a conversational coordination unit 4100, arelational search unit 4200, a semantic interface unit 4300, and asystem access interface unit 4400. The low-latency database analysissystem 4000 may be implemented in part by the low-latency databaseanalysis system 3000 shown in FIG. 3. In an implementation, the systemaccess interface unit 4400 may be a Web client, a mobile client, a chatbot, and similar user interfaces. In some implementations, the Webclient may be a client, such as a TypeScript or JavaScript client, forconversational interfacing with the low-latency database analysis system4000. In an example, the system access interface unit 4400 may be incommunication with the conversational coordination unit 4100.

In an example, the relational search unit 4200 may obtain dataexpressing a usage intent with respect to the low-latency databaseanalysis system 4000 from the system access interface unit 4400. In someimplementations, such as for a mobile client, the relational search unit4200 may obtain the data from the conversational coordination unit 4100,which received the data from the system access interface unit 4400. Insome implementations, the data may be received via natural language,voice recognition systems, and the like. The data expressing the usageintent may include a current request string expressed in a naturallanguage, a current context associated with the current request string,and a previously generated context associated with a previouslygenerated resolved-request. In some implementations, the relationalsearch unit 4200 may receive the data expressing the usage intent fromthe system access interface unit 4400, from the semantic interface unit4300, or from the conversational coordination unit 4100. For example,the relational search unit 4200 may receive or access the dataexpressing the usage intent in a request for data message or signal.

The relational search unit 4200 may parse the data. If the relationalsearch unit 4200 is unable to parse the data, the low-latency databaseanalysis system 4000 may generate an error message. In animplementation, the relational search unit 4200 may send the data to anatural language processing unit, such as for example natural languageprocessing unit 3710 of FIG. 3. The natural language processing unit maysend the processed data to the low-latency database analysis system 4000for further processing in accordance with the disclosure and descriptionherein. If the relational search unit 4200 is able to parse the data,then the relational search unit 4200 may identify a probabilistic intentfor the data. For example, intent identification may be implemented byidentifying, from the current request string, a conversational phrasecorresponding to a conversational phrase pattern from a defined set ofconversational phrase patterns. In some implementations, the relationalsearch unit 4200 may include an intent unit or processor which performsdata parsing and intent identification. In an example, the intent unitor a service may be implemented via a remote procedure call (RPC)interface exposed by the relational search unit 4200. If the relationalsearch unit 4200 is unable to process the identified intent, thelow-latency database analysis system 4000 may generate an error message.For example, if the identified intent is to share a context with JohnDoe but John Doe is not a valid entry, then the low-latency databaseanalysis system 4000 may send an error message.

The semantic interface unit 4300 may provide data related to answersresponsive to the identified intent of the data. In an example, thesemantic interface unit 4300 may include an OnCommand API for processingidentified intents which initiate actions with respect to a context suchas the current context associated with the current request string or apreviously generated context associated with a previously generatedresolved-request, such as for example, pinning the context, sharing thecontext or changing the chart types of the context.

The conversational coordination unit 4100 may be a Node.JS based serveror the like for conversational interfacing between the low-latencydatabase analysis system 4000 and the system access interface unit 4400.For example, the conversational coordination unit 4100 maybe aserver-side headless browser instance (per-active user) which connectsto the low-latency database analysis system 4000. The conversationalcoordination unit 4100 may expose an API, for example, which facilitatesexchange of the data, context, and the response which may containresults data. The conversational coordination unit 4100 may handledata-request phrases, request-transformation phrases, serial-requestphrases, autonomous-analysis phrases, and action phrases.

The relational search unit 4200, the semantic interface unit 4300, andthe conversational coordination unit 4100 cooperatively or incoordination may generate and output context, mappings, tokens, keywordsresponsive to the identified intent. The low-latency database analysissystem 4000 accumulates and concatenates multiple contexts, mappings,tokens, keywords, state identification and related information tomaintain a conversational context flow for subsequent user inputs ofdata against previous context, mappings, tokens, keywords. For example,the accumulated contexts, mappings, tokens, keywords, stateidentification and related information, data or information may behandled in an object which is accessible by at least the relationalsearch unit 4200 and/or the conversational coordination unit 4100. In anexample, the object may be a table, data structure, data construct,queue and the like. The relational search unit 4200 and/or theconversational coordination unit 4100 uses the properties in thecontexts to curate the accumulated context from previous requests fordata.

A follow-up request for data may be operated on or applied against theprevious context or further requests for data may be executed relativeto the previous context, mappings, tokens, keywords to address adifferent but related aspect of the previous output context, mappings,tokens, keywords. For example, processing of the identified intent maychange the answer (response) state, application state or require actionas described by the identified intent.

In some implementations, the conversational phrase patterns may includea data-request phrase, a request-transformation phrase, a serial-requestphrase, an autonomous-analysis phrase, and an action phrase. Forexample, the data-request phrase may generate a resolved-request withoutusing the previously generated context associated with a previouslygenerated resolved-request. In an example, there may have been noprevious conversational context as a user may have just initiated asession on the low-latency database analysis system 4000. In an example,there is a previous context. The relational search unit 4200 mayidentify the request for data as new based on the pattern matched. Thepattern matched indicating that the request for data and the previouscontext are unrelated. In this later example, the previous context iserased as the request for data is a fresh request for data.

For example, the request-transformation phrase may generate aresolved-request by modifying a previously generated resolved-request. Arequest-transformation phrase, for example, may include requests fordata that modify the previously generated resolved-request in a certainway. The request-transformation phrases may include requests for data toadd a column to the previously generated context, remove a column fromthe previously generated context, add a filter, remove a filter on acolumn, remove all filters, change a filter value, i.e. north instead ofsouth, sort on a set of columns, sort columns in an ascending/descendingorder, change date bucketing, limit to top N, exclude value(s), drilldown by attribute and drill down on a particular filter value by<attribute>. Examples are shown in Table 1

TABLE 1 Requests for data Transformation Construct Add Column 1. [add]<agg> <Column> 2. [By|drill by|group by| breakdown by|split by]<Attribute> Remove Column 1. <remove | delete | erase | take away><column> Add Filter 1. [for|only for| restrict to | limit to|just for]<value> 2. [relational search unit filter] 3. [Exclude|Everything but]value Remove filters on a column 1. For all <column-- plural form> 2.[Remove|drop|earse] filters on <column> Remove all filters 1. [Remove |clear] filters Change filter value 1. [Switch|change] <filter> 2.<filter> instead

FIG. 5 is a flow diagram of an example of a method 5000 forrequest-transformation phrase processing in accordance with thisdisclosure. The method 5000 may be implemented in a low-latency databaseanalysis system such as the low-latency database analysis system 4000shown in FIG. 4. The method 5000 includes generating 5100 a context foran identified request-transformation phrase, maintaining 5200 a previouscontext, generating 5300 a resolved-request by modifying a previouslygenerated resolved-request included in the previous context, obtaining5400 results data responsive to the resolved-request from a distributedin-memory database, generating 5500 a response including the resultsdata and the context, and outputting 5600 the response.

The method 5000 includes generating 5100 a context for an identifiedrequest-transformation phrase and maintaining 5200 a previous context. Aprevious context exists which was based on a previously generatedresolved-request. The previous context is maintained. A new context isgenerated to contain the request-transformation phrase, which nowbecomes the current context.

The method 5000 includes generating 5300 a resolved-request by modifyinga previously generated resolved-request included in the previouscontext. A relational search unit, such as relational search unit 4200identifies the request-transformation phrase from the current contextand a previous resolved-request from the previous context. Therelational search unit generates the resolved-request by modifying theprevious resolved-request with the request-transformation phrase. Therelational search unit sends the resolved-request to a semanticinterface unit, such as semantic interface unit 4300.

The method 5000 includes obtaining 5400 results data responsive to theresolved-request from a distributed in-memory database. The semanticinterface unit sends a data-query based on the resolved-request to adistributed in-memory dababase such as distributed in-memory dababase3300. The distributed in-memory dababase generates results data based onthe data-query. The distributed in-memory dababase sends the resultsdata to the semantic interface unit.

The method 5000 includes generating 5500 a response including theresults data and the context and outputting 5600 the response. Thiscontext is now the current context.

For example, the serial-request phrase may generate a resolved-requestsuch that the resolved-request identifies a result of the previouslygenerated context associated with a previously generatedresolved-request as a data-source for the resolved-request. Theserial-request phrase use the results data of a previousresolved-request to answer a new question. This may look like, forexample: For those <attribute|value> <query>.

FIG. 6 is a flow diagram of an example of a method 6000 forserial-request phrase processing in accordance with this disclosure. Themethod 6000 may be implemented in a low-latency database analysis systemsuch as the low-latency database analysis system 4000 shown in FIG. 4.The method 6000 includes generating 6100 a context for an identifiedserial-request phrase, maintaining 6200 a previous context, obtaining6300 results data responsive to a previously generated resolved-requestfrom a distributed in-memory database; storing 6400 the results data;generating 6500 a resolved-request which uses the results data as adata-source, obtaining 6600 results data responsive to theresolved-request from a distributed in-memory database, generating 6700a response including the results data and the context, and outputting6800 the response.

The method 6000 includes generating 6100 a context for an identifiedserial-request phrase and maintaining 6200 a previous context. Aprevious context exists which was based on a previously generatedresolved-request. The previous context is maintained. A new context isgenerated to contain the serial-request phrase, which is now the currentcontext.

The method 6000 includes obtaining 6300 results data responsive to apreviously generated resolved-request from a distributed in-memorydatabase. A relational search unit, a semantic interface unit, and adistributed in-memory database execute a previously generatedresolved-request included in the previous context.

The method 6000 includes storing 6400 the results data. In someimplementations, the results data is stored locally in memory in therelational search unit.

The method 6000 includes generating 6500 a resolved-request which usesthe results data as a data-source. The relational search unit generatesa resolved-request which uses the stored results data as a data-source.

The method 6000 includes obtaining 6600 results data responsive to theresolved-request from the distributed in-memory database. The semanticinterface unit sends a data-query based on the resolved-request to adistributed in-memory dababase such as distributed in-memory dababase3300. The distributed in-memory dababase generates results data based onthe data-query. The distributed in-memory dababase sends the resultsdata to the semantic interface unit.

The method 6000 includes generating 6700 a response including theresults data and the context and outputting 6800 the response.

For example, the autonomous-analysis phrase may generate aresolved-request such that the resolved-request indicates a request forautonomous-analysis based on a previously generated resolved-request.The autonomous-analysis phrase may look like, for example, “How is Xdoing” (auto analyze) or “Why did X increase/decrease” (ComparativeAnalysis).

FIG. 7 is a flow diagram of an example of a method 7000 forautonomous-analysis phrase processing in accordance with thisdisclosure. The method 7000 may be implemented in a low-latency databaseanalysis system such as the low-latency database analysis system 4000shown in FIG. 4. The method 7000 includes generating 7100 a context foran identified autonomous-analysis phrase, maintaining 7200 a previouscontext, automatically identifying 7300 one or more insights using thecurrent context and the previous context; and outputting 7400 theinsights.

The method 7000 includes generating 7100 a context for anautonomous-analysis phrase and maintaining 7200 a previous context. Aprevious context exists which was based on a previous resolved-request.The previous context is maintained. A new context is generated tocontain the autonomous-analysis phrase, which is now the currentcontext.

The method 7000 includes automatically identifying 7300 one or moreinsights using the current context and the previous context. An insightunit such as insight unit 3730 identifies one or more insights, based onthe current context and the previous context, where the insights may bedata other than data expressly requested by a user, and which may beidentified and prioritized, or both, based on probabilistic utility.

The method 7000 includes outputting 7400 the insights.

For example, the action phrase may identify the previously generatedresolved-request as the resolved request and generates an action-requestcorresponding to the action phrase. The action phrase may includeactions, such as for example, pinning the context, sharing the contextor changing the chart types of the context.

FIG. 8 is a flow diagram of an example of a method 8000 for actionphrase processing in accordance with this disclosure. The method 8000may be implemented in a low-latency database analysis system such as thelow-latency database analysis system 4000 shown in FIG. 4. The method8000 includes generating 8100 a context for an identified action phrase,maintaining 8200 a previous context, and performing 8300 an operationbased on the action phrase.

The method 8000 includes generating 8100 a context for an action phraseand maintaining 8200 a previous context. A previous context exists whichwas based on a previous resolved-request. The previous context ismaintained. A new context is generated to contain the action phrase,which is now the current context.

The method 8000 includes performing 8300 an operation based on theaction phrase. In some implementations, performance of the operation,such as a pin or share, would return the same results data. Anindication of whether the operation was successful or a failure is sentor transmitted to the user. In some implementations, performance of theoperation, such as chart type, the previously generated resolved-requestis used with a new visualization context, for example.

The low-latency database analysis system 4000 identifies a probabilisticintent of data expressing a usage intent. “Intent” is a property of asentence that represents the main intention with which the user framedthe sentence. An intent is made up of: 1) language signals like parts ofspeech (e.g. Adjective, Noun etc), and word relations (e.g. noun subject(e.g. “which director made most movies”) and preposition object (e.g.movies made in 2010); 2) intent representing words like, “share”, “pin”,“sort” etc; and 3) 1) and 2) above occurring in a particular sequence,for example, sort this by revenue.

Intent is composed of entities, which need to be identified before theyare matched against a set of templates, which are termed conversationalphrase patterns. Based on the confidence of the templates that matched,an intent is recognized and understood.

A conversational phrase pattern is a template that encodes a set ofrules that are to be used to classify a natural language sentence to anintent. Each conversational phrase pattern represents a unique intentwith a certain confidence. For example, confidence values may bestatically assigned based on precision in identification of the intentthat the conversational phrase pattern represents.

An example is presented using the natural language query “Color hopescholar students in red.” FIG. 9 is an example parsing 9000 of an inputsentence in accordance with this disclosure. From this request for data,properties are extracted, which are used to match against a pattern. Thenatural language request for data is parsed, in part, using, forexample, Tensorflow®, Syntaxnet, or like systems. From the informationfetched, properties like “relation” (e.g. color: NN), parts of speech(e.g. student: NOUN) are used to match rules defined in a pattern.Additional reference is made tohttps://cloud.google.com/natural-language/, which is incorporated hereinby reference.

Intent representing words. In the example request for data, “Color” isan explicit action to change the colors of a subset of the visualizationthat the user is currently looking it. The expected response fromrecognizing the intent could be to pass a custom chart configurationback to the client, which is able to update colors in say, a bar chart.

A typical pattern that can be used to capture the presented examplerequest for data can be as follows: “Color” <boolean entity> <Attributeentity & present in current visualization> <preposition> <An entityrecognized as a color>.

In an example, entity could represent a “Column Name” in a table that arequest for data can match. For example, “students” in the examplerequest for data above could represent the column name “Student Id” in ahigh school database. In another example, entity could represent a“Value” in a table column, e.g. “California” is a “value” in a column“State” in a credit-card transactions table.

Intents, as stated above, are represented as patterns. For example, acollection of intents and patterns that capture them, are collectedbased on observed user commands to the system. As more data is collectedand this behavior is exposed to larger audiences, addition andrepresentation of new intents will be possible. Intents that arerecognized from requests for data may include, for example, the additionor removal of a sorting column to a visualization, the addition orremoval of a column to a visualization, changing the chart type of avisualization, addition or removal of a filter to the visualization,e.g. “Filter this on close date last 3 quarters”, share the currentvisualization with user/group, ability to trigger automatic databaseanalysis for insights on the request for data representing the currentvisualization, ability to ask follow up questions based on the resultdisplayed as a part of the visualization. For example, a first requestfor data may ask: “list all horror movies made in 2017”, and a secondrequest for data may ask “in those movies which grossed more than abillion?” As stated above, intent may be inferred using patternmatching.

The relational search unit 4200 uses phrases enroute to constructing aconstruct which is used by the semantic interface unit 4300 to query adistributed in-memory database such the distributed in-memory database3300 of FIG. 3. A phrase can be represented as a sequence of tokens,which together represent something meaningful or useful. For example,“Top 10 movie budget” is a top-bottom phrase, which lists the top 10movies sorted by budget.

In the English language, unlike the relational search unit 4200, thetokens contributing to a valid relational search unit phrase may not benext to each other. The signals from the matches and grammaticalrelations between words are used in the input query to construct validrelational search unit-phrases.

English sentence construction rules provide few signals, in terms of thekind of terminology to expect in the presence of the words before theword itself. For example, “how much <w>” gives a signal that <w> is mostlikely a “uncountable” quantifier which could be mapped to a numericalattribute or a measure.

Different interpretations of input query are boosted using common wordsequences. The low-latency database analysis system 4000 can then usesignals from natural language input data in terms of intentidentification for a request for data. For example, from the inputsentence “Which movie made most money?” there are multiple equally goodprefix matches for the token “movie”, like “movie_title”,“movie_facebook_likes” etc. Given this input sentence, it is known that“which movie” would most likely map “movie” to an entity than a number,hence allowing a pattern matcher unit, for example in relational searchunit 10020, to prefer “movie_title” over “movie_facebook_likes”.

FIG. 10 is a block diagram of an example of a PhrasePatternMatch 10000which may use pattern matchers such as, for example, aSimplePatternMatcher 10100, a TreePatternMatcher 10200, and aSequenceMatcher 10300 in accordance with this disclosure.

In pattern matching, a Phrase Pattern is a pattern, represented by aphrase pattern proto definition, consisting of a repeated list ofPhrasePatternElement, which provide information on how each element ismatched respectively. An example is shown in Table 2.

TABLE 2 pattern { pattern_type: DEPENDENCY_FOREST phrase_type:AGGREGATED_COLUMN_PHRASE pattern_score: 5 phrase_pattern_element {payload { pos_token { token_type: ATTRIBUTE } } parent_index: −1 type:POS_TOKEN logical_form_payload { logical_form_element_type: ENTITY } }phrase_pattern_element { payload { pos_token { token_type: KEYWORD text:“count” } } parent_index: 0 type: POS_TOKEN logical_form_payload {logical_form_element_type: OPERATOR translation_index:KEYWORD_TRANSLATION } } }

A PhrasePatternMatch is the object which has the result of a successfulmatch with a Phrase Pattern.

A logical form represents an encoding of various functions, which areinferred by the pattern matcher. The functions include at least the typeof operation performed, entity that the operation is applied on andmodifier on which the entity is manipulated. Each match returned aftermatching a Phrase Pattern, is encoded into a logical form.

A Sentence Proto is an output from a parser such as SyntaxNet, whichencodes the relationships and parts of speech information of differentwords in the input query.

The inputs to the pattern matcher include at least a Phrase Pattern thatis to be matched against, a Sentence Proto representation of input queryfrom the parser, a match handler, which decides what kind of matchingneeds to done at an entity level and InputQuery to token mapper, whichmaps indices from words in input query to tokens resolved by therelation search unit. For example, the match handler may be one of theSimplePatternMatcher 10100, the TreePatternMatcher 10200, and theSequenceMatcher 10300.

The output from the pattern matcher include at least a list of objectsof PhrasePatternMatch, which records all the matches for individualelements of a phrase pattern, and a list of objects ofPhrasePatternError, which records errors or misses in pattern matchingagainst the passed pattern.

Every PhrasePatternMatch generates a subset of the items describedabove, which help in encoding the signals captured from input query,parser, and relational search unit matching, to a functionalrepresentation.

As an example, the input request for data may be “How many moviesgrossed more than 100,000?” For a PhrasePatternMatch (matching theunderlined words), a comparison function is formed such as: GREATER_THAN(gross, 100,000). This functional structure has the information abouthow they are to be serialized into a list of tokens, which areunderstood by the relational search unit. In the example, the tokensserialized by the function would be: gross>100,000.

The SimplePatternMatcher 10100 is a loose pattern matching strategy,which allows the capability to match elements anywhere in the input andreturn the corresponding PhrasePatternMatch. For example, for the inputsentence “What movie made the highest gross in 2013?”, a loose patternmatching an attribute (movie→movie_title), measure (gross) andsuperlative word (highest), will match the underlined terms and create arelational search unit-phrase top 10 movie_title budget. This type ofmatcher will result in outputs with high recall and low precision.

The SequencePatternMatcher 10200 matches sequences oftokens/text/Syntaxnet signals in the input request for data. Forexample, for an input sentence “How much budget was allocated toTitanic?”, a sequence pattern matching text “how much” and a “measure”in sequence will match the underlined terms. This type of matcher willresult in outputs with low recall and very high precision.

The TreePatternMatcher 10300 matches a defined tree structure, whererelationships are defined in terms by Syntaxnet, for example. Thepattern describing a tree (or forest) is matched against the tree (orforest) structure of the input query and matches are recorded.

For example, for the input sentence “Which was the movie with highestgross?”, a relevant part of the parser output of the query is shown inTable 3:

TABLE 3 token { raw_text: “movie” pos_tag: NOUN  parent_index: −1relation: “ROOT” word_index: 3 } token { raw_text: “with” pos_tag:PREPOSITION parent_index: 3 relation: “prep” word_index: 4 } token {raw_text: “highest” pos_tag: ADJECTIVE_SUPERLATIVE parent_index: 6relation: “amod” word_index: 5 } token { raw_text: “gross” pos_tag:ADJECTIVE parent_index: 4 relation: “pobj” word_index: 6 }

A crucial signal here is that the “superlative” word (highest) is achild of “measure” (gross). If a pattern tries to match a tree structureinvolving 2 nodes as shown in Table 4, then a match for the underlinedwords will be obtained. This type of matcher will result in outputs withdecent recall and high precision.

TABLE 4 Measure(root) , attribute(root) \ Superlative

FIG. 11 is a flow diagram of an example of a method 11000 forconversational database analysis in accordance with this disclosure. Themethod 11000 may be implemented in a low-latency database analysissystem such as the low-latency database analysis system 4000 shown inFIG. 4. The method 11000 includes obtaining 11100 data expressing ausage intent with respect to the low-latency database analysis system,identifying 11200, from the current request string, a conversationalphrase corresponding to a conversational phrase pattern from a definedset of conversational phrase patterns, generating 11300 aresolved-request based on the identified conversational phrase,including 11400 the resolved-request in the current context, obtaining11500 results data responsive to the resolved-request from a distributedin-memory database, generating 11600 a response including the resultsdata and the current context, and outputting 11700 the response.

The method 13000 includes obtaining 11100 data expressing a usage intentwith respect to the low-latency database analysis system. The dataexpressing the usage intent includes a current request string expressedin a natural language, a current context associated with the currentrequest string, and a previously generated context associated with apreviously generated resolved-request.

The method 13000 includes identifying 11200, from the current requeststring, a conversational phrase corresponding to a conversational phrasepattern from a defined set of conversational phrase patterns. Thedefined set of conversational phrase patterns may include, but are notlimited to, a data-request phrase, a request-transformation phrase, aserial-request phrase, an autonomous-analysis phrase, and an actionphrase. The pattern identification is performed as described withrespect to at least FIGS. 5-10.

The method 13000 includes generating 11300 a resolved-request based onthe identified conversational phrase. A resolved-request is generatedbased on the identified conversational phrase pattern. For example, inresponse to a determination that the conversational phrase is adata-request phrase, a resolved-request is generated based on thedata-request phrase by omitting the previously generated context. Forexample, in response to a determination that the conversational phraseis a request-transformation phrase, a resolved-request is generated bymodifying the previously generated resolved-request based on therequest-transformation phrase. In some implementations, the method 5000of FIG. 5 may be used. For example, in response to a determination thatthe conversational phrase is a serial-request phrase, a resolved-requestis generated based on the serial-request phrase such that theresolved-request identifies a result of the previously generatedresolved-request as a data-source for the resolved-request. In someimplementations, the method 6000 of FIG. 6 may be used. For example, inresponse to a determination that the conversational phrase is anautonomous-analysis phrase, a resolved-request is generated such thatthe resolved-request indicates a request for autonomous-analysis basedon the previously generated resolved-request. In some implementations,the method 7000 of FIG. 7 may be used. For example, in response to adetermination that the conversational phrase is an action phrase, aresolved-request is generated which includes identifying the previouslygenerated resolved-request as the resolved request and generating anaction-request corresponding to the action phrase, the action-requestreferring to the previously generated resolved-request. In someimplementations, the method 8000 of FIG. 8 may be used. A relationalsearch unit, such as relational search unit 4200 generates theresolved-request based on the identified conversational phase pattern.The relational search unit sends the resolved-request to a semanticinterface unit, such as semantic interface unit 4300.

The method 13000 includes including 11400 the resolved-request in thecurrent context.

The method 13000 includes obtaining 11500 results data responsive to theresolved-request from a distributed in-memory database. The semanticinterface unit sends a data-query based on the resolved-request to adistributed in-memory dababase such as distributed in-memory dababase3300. The distributed in-memory dababase generates results data based onthe data-query. The distributed in-memory dababase sends the resultsdata to the semantic interface unit.

The method 13000 includes generating 11600 a response including theresults data and the current context and outputting 11700 the response.

As used herein, the terminology “computer” or “computing device”includes any unit, or combination of units, capable of performing anymethod, or any portion or portions thereof, disclosed herein.

As used herein, the terminology “processor” indicates one or moreprocessors, such as one or more special purpose processors, one or moredigital signal processors, one or more microprocessors, one or morecontrollers, one or more microcontrollers, one or more applicationprocessors, one or more central processing units (CPU)s, one or moregraphics processing units (GPU)s, one or more digital signal processors(DSP)s, one or more application specific integrated circuits (ASIC)s,one or more application specific standard products, one or more fieldprogrammable gate arrays, any other type or combination of integratedcircuits, one or more state machines, or any combination thereof.

As used herein, the terminology “memory” indicates any computer-usableor computer-readable medium or device that can tangibly contain, store,communicate, or transport any signal or information that may be used byor in connection with any processor. For example, a memory may be one ormore read only memories (ROM), one or more random access memories (RAM),one or more registers, low power double data rate (LPDDR) memories, oneor more cache memories, one or more semiconductor memory devices, one ormore magnetic media, one or more optical media, one or moremagneto-optical media, or any combination thereof.

As used herein, the terminology “instructions” may include directions orexpressions for performing any method, or any portion or portionsthereof, disclosed herein, and may be realized in hardware, software, orany combination thereof. For example, instructions may be implemented asinformation, such as a computer program, stored in memory that may beexecuted by a processor to perform any of the respective methods,algorithms, aspects, or combinations thereof, as described herein.Instructions, or a portion thereof, may be implemented as a specialpurpose processor, or circuitry, that may include specialized hardwarefor carrying out any of the methods, algorithms, aspects, orcombinations thereof, as described herein. In some implementations,portions of the instructions may be distributed across multipleprocessors on a single device, on multiple devices, which maycommunicate directly or across a network such as a local area network, awide area network, the Internet, or a combination thereof.

As used herein, the terminology “determine” and “identify,” or anyvariations thereof, includes selecting, ascertaining, computing, lookingup, receiving, determining, establishing, obtaining, or otherwiseidentifying or determining in any manner whatsoever using one or more ofthe devices and methods shown and described herein.

As used herein, the terminology “example,” “embodiment,”“implementation,” “aspect,” “feature,” or “element” indicates serving asan example, instance, or illustration. Unless expressly indicated, anyexample, embodiment, implementation, aspect, feature, or element isindependent of each other example, embodiment, implementation, aspect,feature, or element and may be used in combination with any otherexample, embodiment, implementation, aspect, feature, or element.

As used herein, the terminology “or” is intended to mean an inclusive“or” rather than an exclusive “or.” That is, unless specified otherwise,or clear from context, “X includes A or B” is intended to indicate anyof the natural inclusive permutations. That is, if X includes A; Xincludes B; or X includes both A and B, then “X includes A or B” issatisfied under any of the foregoing instances. In addition, thearticles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from the context to be directed to asingular form.

Further, for simplicity of explanation, although the figures anddescriptions herein may include sequences or series of steps or stages,elements of the methods disclosed herein may occur in various orders orconcurrently. Additionally, elements of the methods disclosed herein mayoccur with other elements not explicitly presented and described herein.Furthermore, not all elements of the methods described herein may berequired to implement a method in accordance with this disclosure.Although aspects, features, and elements are described herein inparticular combinations, each aspect, feature, or element may be usedindependently or in various combinations with or without other aspects,features, and elements.

Although some embodiments herein refer to methods, it will beappreciated by one skilled in the art that they may also be embodied asa system or computer program product. Accordingly, aspects of thepresent invention may take the form of an entirely hardware embodiment,an entirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “processor,”“device,” or “system.” Furthermore, aspects of the present invention maytake the form of a computer program product embodied in one or morecomputer readable mediums having computer readable program code embodiedthereon. Any combination of one or more computer readable mediums may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium include the following: an electrical connection havingone or more wires, a portable computer diskette, a hard disk, a randomaccess memory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), an optical fiber, a portablecompact disc read-only memory (CD-ROM), an optical storage device, amagnetic storage device, or any suitable combination of the foregoing.In the context of this document, a computer readable storage medium maybe any tangible medium that can contain or store a program for use by orin connection with an instruction execution system, apparatus, ordevice.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to CDs, DVDs,wireless, wireline, optical fiber cable, RF, etc., or any suitablecombination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Attributes may comprise any data characteristic, category, content, etc.that in one example may be non-quantifiable or non-numeric. Measures maycomprise quantifiable numeric values such as sizes, amounts, degrees,etc. For example, a first column containing the names of states may beconsidered an attribute column and a second column containing thenumbers of orders received for the different states may be considered ameasure column.

Aspects of the present embodiments are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a computer, such as a special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmeans for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks. These computer programinstructions may also be stored in a computer readable medium that candirect a computer, other programmable data processing apparatus, orother devices to function in a particular manner, such that theinstructions stored in the computer readable medium produce an articleof manufacture including instructions which implement the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer program instructions may also be loaded onto a computer, otherprogrammable data processing apparatus, or other devices to cause aseries of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. The flowcharts and block diagrams in thefigures illustrate the architecture, functionality, and operation ofpossible implementations of systems, methods and computer programproducts according to various embodiments of the present invention. Inthis regard, each block in the flowchart or block diagrams may representa module, segment, or portion of code, which comprises one or moreexecutable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

While the disclosure has been described in connection with certainembodiments, it is to be understood that the disclosure is not to belimited to the disclosed embodiments but, on the contrary, is intendedto cover various modifications and equivalent arrangements includedwithin the scope of the appended claims, which scope is to be accordedthe broadest interpretation so as to encompass all such modificationsand equivalent structures as is permitted under the law.

What is claimed is:
 1. A method for use in a low-latency database analysis system, the method comprising: obtaining data expressing a usage intent with respect to the low-latency database analysis system, wherein the data expressing the usage intent includes a current request string expressed in a natural language, a current context associated with the current request string, and a previously generated context associated with a previously generated resolved-request; identifying, from the current request string, a conversational phrase corresponding to a conversational phrase pattern from a defined set of conversational phrase patterns; generating a resolved-request based on the identified conversational phrase; including the resolved-request in the current context; obtaining results data responsive to the resolved-request from a distributed in-memory database; generating a response including the results data and the current context; and outputting the response.
 2. The method of claim 1, wherein generating the resolved-request comprising: in response to a determination that the conversational phrase is a data-request phrase, generating the resolved-request based on the data-request phrase, wherein generating the resolved-request omits using the previously generated context.
 3. The method of claim 1, wherein generating the resolved-request comprising: in response to a determination that the conversational phrase is a request-transformation phrase, generating the resolved-request by modifying the previously generated resolved-request based on the request-transformation phrase.
 4. The method of claim 1, wherein the request-transformation phrase includes one or more of remove a column from the previously generated context, add a filter, remove a filter on a column, remove all filters, change a filter value, sort on a set of columns, sort columns in an ascending order, sort columns in a descending order, change date bucketing, limit to top N, exclude one or more value, drill down by an attribute, and drill down on a particular filter value by an attribute.
 5. The method of claim 1, wherein generating the resolved-request comprising: in response to a determination that the conversational phrase is a serial-request phrase, generating the resolved-request based on the serial-request phrase such that the resolved-request identifies a result of the previously generated resolved-request as a data-source for the resolved-request.
 6. The method of claim 5, wherein generating the resolved-request comprising: obtaining results data responsive to a previously generated resolved-request from a distributed in-memory database.
 7. The method of claim 6, wherein generating the resolved-request comprising: locally storing, with respect to a relational search unit, the results data.
 8. The method of claim 1, wherein generating the resolved-request comprising: in response to a determination that the conversational phrase is an autonomous-analysis phrase, generating the resolved-request such that the resolved-request indicates a request for autonomous-analysis based on the previously generated resolved-request.
 9. The method of claim 1, wherein the request for autonomous-analysis identifies one or more insights based on the previously generated resolved-request, where the one or more insights are data other than data expressly requested by a user.
 10. The method of claim 1, wherein generating the resolved-request comprising: in response to a determination that the conversational phrase is an action phrase, generating the resolved-request includes identifying the previously generated resolved-request as the resolved request and generating an action-request corresponding to the action phrase, the action-request referring to the previously generated resolved-request.
 11. The method of claim 10, wherein the action phrase includes one or more of pinning the previously generated context, sharing the previously generated context, or changing a chart type of the previously generated context.
 12. A system comprising: a low-latency database; and a processor, the processor configured to: obtain data expressing a usage intent with respect to the low-latency database analysis system, wherein the data expressing the usage intent includes a current request string expressed in a natural language, a current context associated with the current request string, and a previously generated context associated with a previously generated resolved-request; identify, from the current request string, a conversational phrase corresponding to a conversational phrase pattern from a defined set of conversational phrase patterns; generate a resolved-request based on the identified conversational phrase; include the resolved-request in the current context; obtain results data responsive to the resolved-request from a distributed in-memory database; generate a response including the results data and the current context; and output the response.
 13. The system of claim 12, wherein the processor further configured to: in response to a determination that the conversational phrase is a data-request phrase, generate the resolved-request based on the data-request phrase, wherein generating the resolved-request omits using the previously generated context.
 14. The system of claim 12, wherein the processor further configured to: in response to a determination that the conversational phrase is a request-transformation phrase, generate the resolved-request by modifying the previously generated resolved-request based on the request-transformation phrase.
 15. The system of claim 12, wherein the processor further configured to: in response to a determination that the conversational phrase is a serial-request phrase, generate the resolved-request based on the serial-request phrase such that the resolved-request identifies a result of the previously generated resolved-request as a data-source for the resolved-request.
 16. The system of claim 12, wherein the processor further configured to: in response to a determination that the conversational phrase is an autonomous-analysis phrase, generate the resolved-request such that the resolved-request indicates a request for autonomous-analysis based on the previously generated resolved-request.
 17. The system of claim 12, wherein the processor further configured to: in response to a determination that the conversational phrase is an action phrase, generate the resolved-request includes identifying the previously generated resolved-request as the resolved request and generating an action-request corresponding to the action phrase, the action-request referring to the previously generated resolved-request.
 18. A method for use in a low-latency database analysis system, the method comprising: obtaining data expressing a usage intent with respect to the low-latency database analysis system, wherein the data expressing the usage intent includes a current request string expressed in a natural language, a current context associated with the current request string, and a previously generated context associated with a previously generated resolved-request; identifying, from the current request string, a conversational phrase corresponding to a conversational phrase pattern from a defined set of conversational phrase patterns; generating a resolved-request based on the identified conversational phrase, wherein the generating a resolved-request based on the identified conversational phrase further comprising: in response to a determination that the conversational phrase is a data-request phrase, generating the resolved-request based on the data-request phrase by omitting the previously generated context, in response to a determination that the conversational phrase is a request-transformation phrase, generating the resolved-request by modifying the previously generated resolved-request based on the request-transformation phrase, in response to a determination that the conversational phrase is a serial-request phrase, generating the resolved-request based on the serial-request phrase such that the resolved-request identifies a result of the previously generated resolved-request as a data-source for the resolved-request, in response to a determination that the conversational phrase is an autonomous-analysis phrase, generating the resolved-request such that the resolved-request indicates a request for autonomous-analysis based on the previously generated resolved-request, and in response to a determination that the conversational phrase is an action phrase, generating the resolved-request includes identifying the previously generated resolved-request as the resolved request and generating an action-request corresponding to the action phrase, the action-request referring to the previously generated resolved-request; including the resolved-request in the current context; obtaining results data responsive to the resolved-request from a distributed in-memory database; generating a response including the results data and the current context; and outputting the response.
 19. The method of claim 18, wherein the request-transformation phrase includes one or more of remove a column from the previously generated context, add a filter, remove a filter on a column, remove all filters, change a filter value, sort on a set of columns, sort columns in an ascending order, sort columns in a descending order, change date bucketing, limit to top N, exclude one or more value, drill down by an attribute, and drill down on a particular filter value by an attribute.
 20. The method of claim 18, wherein the action phrase includes one or more of pinning the previously generated context, sharing the previously generated context, or changing the chart types of the previously generated context. 